Indexing data at a data intake and query system based on a node capacity threshold

ABSTRACT

As an indexer indexes and groups events, it can generate data slices that include events. Based on a slice rollover policy, the indexer can add a particular slice to an aggregate slice. Based on an aggregate slice backup policy, the indexer can store a copy of the aggregate slice to a shared storage system. The aggregate slice can be used for restore purposes in the event the indexer fails or becomes unresponsive.

RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification. This application, U.S. application Ser. No.16/945,645, was on Jul. 31, 2020 concurrently with the following U.S.Patent Applications, each of which is incorporated by reference hereinin its entirety:

U.S. application Ser. No. Patent Application Title Filing Date16/945,646 INGESTION NODES IN A DATA INTAKE Jul. 31, 2020 AND QUERYSYSTEM 16/945,631 USING A DATA STORE AND MESSAGE Jul. 31, 2020 QUEUE TOINGEST DATA FOR A DATA INTAKE AND QUERY SYSTEM 16/945,578 GENERATING ANDSTORING Jul. 31, 2020 AGGREGATE DATA SLICES IN A REMOTE SHARED STORAGESYSTEM

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which likereference numerals indicate similar elements and in which:

FIG. 1 is a block diagram of an example networked computer environment,in accordance with example embodiments.

FIG. 2A is a block diagram of an example data intake and query system,in accordance with example embodiments.

FIG. 2B is a block diagram of an example data intake and query system,in accordance with example embodiments.

FIG. 3 is a block diagram of an example cloud-based data intake andquery system, in accordance with example embodiments.

FIG. 4 is a block diagram of an example data intake and query systemthat performs searches across external data systems, in accordance withexample embodiments.

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments.

FIGS. 5B and 5C are block diagrams illustrating embodiments of variousdata structures for storing data processed by the data intake and querysystem.

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments.

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleembodiments.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments.

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed embodiments.

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example embodiments.

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example embodiments.

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example embodiments.

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example embodiments.

FIGS. 9, 10, 11A, 11B, 11C, 11D, 12, 13, 14, and 15 are interfacediagrams of example report generation user interfaces, in accordancewith example embodiments.

FIG. 16 is an example search query received from a client and executedby search peers, in accordance with example embodiments.

FIG. 17A is an interface diagram of an example user interface of a keyindicators view, in accordance with example embodiments.

FIG. 17B is an interface diagram of an example user interface of anincident review dashboard, in accordance with example embodiments.

FIG. 17C is a tree diagram of an example a proactive monitoring tree, inaccordance with example embodiments.

FIG. 17D is an interface diagram of an example a user interfacedisplaying both log data and performance data, in accordance withexample embodiments.

FIG. 18 is a data flow diagram illustrating an embodiment of the dataflow and communications between a variety of the components of adistributed data processing system, such as the data intake and querysystem to generate and place events in a message bus.

FIG. 19 is a flow diagram illustrative of an embodiment of a routineimplemented by a computing device of a distributed data processingsystem, for communicating groups of events to a message bus.

FIG. 20 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem, for communicating groups of events to a message bus.

FIG. 21 is a data flow diagram illustrating an embodiment of the dataflow and communications between a variety of the components of adistributed data processing system, such as the data intake and querysystem to store aggregate slices and buckets in a shared storage system.

FIG. 22 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem, for storing aggregate data slices to a shared storage system.

FIG. 23 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem, for asynchronously obtaining and processing a message payloadfrom a message bus.

FIG. 24 is a data flow diagram illustrating an embodiment of data flowand communications illustrating an example method for recoveringpre-indexed data from a shared storage system following a failedindexer.

FIG. 25 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem, recovering pre-indexed data from a shared storage systemfollowing a failed indexer.

FIG. 26 is a data flow diagram illustrating an embodiment of data flowand communications illustrating an example method for identifying datato be searched using a bucket map identifier.

FIG. 27 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem for identifying data to be searched using a bucket mapidentifier.

FIG. 28 is a data flow diagram illustrating an embodiment of data flowand communications illustrating an example method for search recoveryusing a shared storage system following a failed search peer.

FIG. 29 is a flow diagram illustrative of an embodiment of a routine,implemented by a computing device of a distributed data processingsystem, for search recovery using a shared storage system following afailed search peer.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Server System with Ingestor, Message Bus, and            cluster master        -   2.6. Cloud-Based System Overview        -   2.7. Searching Externally-Archived Data        -   2.8. Data Ingestion        -   2.9. Query Processing        -   2.10. Pipelined Search Language        -   2.11. Field Extraction        -   2.12. Example Search Screen        -   2.13. Data Models        -   2.14. Acceleration Technique        -   2.15. Security Features        -   2.16. Data Center Monitoring        -   2.17. IT Service Monitoring    -   3.0. Processing Data Using Ingestors and a Message Bus        -   3.1. Ingestor Data Flow example        -   3.2. Ingestor Flow Examples        -   3.3. Indexer Data Flow example        -   3.4. Indexer Flow examples    -   4.0. Using a Cluster Master and Bucket Map Identifiers to Manage        Data        -   4.1. Recovering Pre-Indexed Data Following a Failed Indexer        -   4.2. Mapping Groups of Data and Indexers to a Bucket Map            Identifier for Searching        -   4.3. Search Recover Using a Shared Storage System Following            a Failed Search Peer    -   5.0. Terminology

1.0. GENERAL OVERVIEW

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine data. Machine data is any data producedby a machine or component in an information technology (IT) environmentand that reflects activity in the IT environment. For example, machinedata can be raw machine data that is generated by various components inIT environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine data can include systemlogs, network packet data, sensor data, application program data, errorlogs, stack traces, system performance data, etc. In general, machinedata can also include performance data, diagnostic information, and manyother types of data that can be analyzed to diagnose performanceproblems, monitor user interactions, and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and search machine datafrom various websites, applications, servers, networks, and mobiledevices that power their businesses. The data intake and query system isparticularly useful for analyzing data which is commonly found in systemlog files, network data, and other data input sources. Although many ofthe techniques described herein are explained with reference to a dataintake and query system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

In the data intake and query system, machine data are collected andstored as “events”. An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system,etc.) that are associated with successive points in time. In general,each event has a portion of machine data that is associated with atimestamp that is derived from the portion of machine data in the event.A timestamp of an event may be determined through interpolation betweentemporally proximate events having known timestamps or may be determinedbased on other configurable rules for associating timestamps withevents.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system uses a flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. Note that a flexible schema may be applied toevents “on the fly,” when it is needed (e.g., at search time, indextime, ingestion time, etc.). When the schema is not applied to eventsuntil search time, the schema may be referred to as a “late-bindingschema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp. The system stores the eventsin a data store. The system enables users to run queries against thestored events to, for example, retrieve events that meet criteriaspecified in a query, such as criteria indicating certain keywords orhaving specific values in defined fields. As used herein, the term“field” refers to a location in the machine data of an event containingone or more values for a specific data item. A field may be referencedby a field name associated with the field. As will be described in moredetail herein, a field is defined by an extraction rule (e.g., a regularexpression) that derives one or more values or a sub-portion of textfrom the portion of machine data in each event to produce a value forthe field for that event. The set of values produced aresemantically-related (such as IP address), even though the machine datain each event may be in different formats (e.g., semantically-relatedvalues may be in different positions in the events derived fromdifferent sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data intake and query system utilizes a late-bindingschema while performing queries on events. One aspect of a late-bindingschema is applying extraction rules to events to extract values forspecific fields during search time. More specifically, the extractionrule for a field can include one or more instructions that specify howto extract a value for the field from an event. An extraction rule cangenerally include any type of instruction for extracting values fromevents. In some cases, an extraction rule comprises a regularexpression, where a sequence of characters forms a search pattern. Anextraction rule comprising a regular expression is referred to herein asa regex rule. The system applies a regex rule to an event to extractvalues for a field associated with the regex rule, where the values areextracted by searching the event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

2.0. OPERATING ENVIRONMENT

FIG. 1 is a block diagram of an example networked computer environment100, in accordance with example embodiments. Those skilled in the artwould understand that FIG. 1 represents one example of a networkedcomputer system and other embodiments may use different arrangements.

The networked computer environment 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some embodiments, one or more client devices 102 are coupled to oneor more host devices 106 and a data intake and query system 108 via oneor more networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, an environment 100 includes one or morehost devices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types of machine data.For example, a host application 114 comprising a web server may generateone or more web server logs in which details of interactions between theweb server and any number of client devices 102 is recorded. As anotherexample, a host device 106 comprising a router may generate one or morerouter logs that record information related to network traffic managedby the router. As yet another example, a host application 114 comprisinga database server may generate one or more logs that record informationrelated to requests sent from other host applications 114 (e.g., webservers or application servers) for data managed by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In some embodiments, each client device 102 may host or execute one ormore client applications 110 that are capable of interacting with one ormore host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more websites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some embodiments, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In some embodiments, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some embodiments, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a data intakeand query system, such as a system 108. In such cases, the provider ofthe system 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication or other users.

In some embodiments, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some embodiments, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets can beread or examined to identify network data contained within the packets,for example, and other aspects of data packets can be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some embodiments, network performance data refers to any type of datathat indicates information about the network and/or network performanceNetwork performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some embodiments, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some embodiments, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2A is a block diagram of an example data intake and query system108, in accordance with example embodiments. System 108 includes one ormore forwarders 204 that receive data from a variety of input datasources 202, one or more indexers 206 that process and store the data inone or more data stores 208, and one or more search heads 210 that areused to search the data in the data stores 208 and/or other data that isaccessible via the data intake and query system 108. The variouscomponents of the data intake and query system 108 can be implemented onseparate computer systems, or any one or any combination of thecomponents may be implemented separate processes executing on one ormore computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by system 108. Examples of a data sources 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc. In some embodiments, each datasource can correspond to data obtained from a different machine, virtualmachine, container, or computer system. In certain embodiments, eachdata source can correspond to a different data file, directories offiles, event logs, or registries, of a particular machine, virtualmachine, container, or computer system.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In certain embodiments, a forwarder 204 may be installed on a datasource 202. In some such embodiments, the forwarder 204 may run in thebackground as the host data source 202 performs its normal functions. Insome embodiments, a forwarder 204 may comprise a service accessible todata sources, such as client devices 102 and/or host devices 106, via anetwork 104. For example, one type of forwarder 204 may be capable ofconsuming vast amounts of real-time data from a potentially large numberof client devices 102 and/or host devices 106. The forwarder 204 may,for example, comprise a computing device which implements multiple datapipelines or “queues” to handle forwarding of network data to indexers206.

Forwarders 204 route data to indexers 206. A forwarder 204 may alsoperform many of the functions that are performed by an indexer 206. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally, or alternatively, a forwarder 204 mayperform routing of events to indexers 206.

Indexers 206 can be implemented as one or more distinct computer systemsor devices and/or as one or more virtual machines, containers, PODS, orother isolated execution environment. The indexers 206 can perform anumber of operations on the data they receive including, but not limitedto, keyword extractions on raw data, removing extraneous data, detectingtimestamps in the data, parsing data, creating events from the data,grouping events to create buckets, indexing events, generatingadditional files, such as inverted indexes or filters to facilitateperformant searching, storing buckets, events, and/or any additionalfiles in the data stores 208, and searching events or data stored in thedata stores 208. Additional functionality of the indexers will bedescribed herein.

The data stores 208 can be implemented as separate and distinct datastores and/or be implemented as part of a shared computing system orcloud storage system, such as, but not limited to Amazon S3, GoogleCloud Storage, Azure Blob Storage, etc. Each data store 208 can beassociated with a particular indexer 206 and store the events, buckets,or other data generated or processed by the particular indexer 206.Accordingly, a data store 208 may contain events derived from machinedata from a variety of sources. The events may all pertain to the samecomponent in an IT environment, and this data may be produced by themachine in question or by other components in the IT environment.

The search head 210 can be implemented as one or more distinct computersystems or devices and/or as one or more virtual machines, containers,PODS, or other isolated execution environment. The search head 210 canreceive search requests from one or more client devices 102 or otherdevices. Based on the received search requests (also referred to hereinas query or search query), the search head 210 can interact with theindexers 206 or other system components to obtain the results of thesearch request. As described herein, the received queries can includefilter criteria for identifying a set of data and processing criteriafor processing the set of data. The processing criteria may transformthe set of data in a variety of ways, as described herein. Additionalfunctionality of the search head 210 will be described herein.

2.5. Data Server System with Ingestor, Message Bus, and Cluster Master

In some cases, forwarders 204 can prefer certain indexers 206 and sendlarge quantities of data to the same indexer 206 even if other indexers206 have more capacity. In such situations, this can decrease thethroughput and performance of the data intake and query system 108. Inaddition, it can be difficult to update forwarders 204 given that theymay be remotely located from the indexers 206, installed on a thirdparty's system, and/or under the control of a third party. Further,given the number of tasks assigned to an indexer 206, if an indexer 206fails, there can be a significant amount of processing to be redone.

Accordingly, in some cases, the data intake and query system 108 caninclude one or more ingestors and a message bus. The ingestors can beseparate from the indexers 206 and perform some of the tasks of theprocessors, such as generating events from data. After generating theevents, the ingestors can group the events and send the groups of eventsto the message bus. The ingestor can also track which events have beensent to the message bus and send an acknowledgement to a forwarder orother source.

Separately, indexers 206 can monitor their capacity to process indexadditional data, and based on a determination that a particular indexer206 has capacity to process additional data, the indexer 206 can requestthe group of events from the message bus, process the group of event,and store the events to a shared storage system 260.

In this way, the data intake and query system can increase itsthroughput, resiliency and performance By splitting event generationtasks (assigned to ingestors) from indexing tasks (assigned toindexers), the system 108 can dynamically and independently scaleingestors to accommodate additional ingestion load and/or independentlyscale indexers to accommodate additional indexing load, therebyincreasing the throughput of the system 108. When the amount ofingestion or indexing load decreases, the system 108 can dynamically andindependently remove ingestors or indexers, respectively, therebyimproving efficiency and resource utilization. Thus, the system 108 canhave a different number of components generating events and indexingevents.

By sending an acknowledgement when the events are on the message bus,the system 108 can reduce the amount of time to send an acknowledgmentof data receipt, thereby improve the system's 108 responsiveness tosources and freeing up resources of the source for other tasks.

In addition, by keeping generated events on the message bus, the system108 can improve resiliency in the event an indexer fails. In such ascenario, because the events are already generated and available,another indexer 206 can skip event generation tasks and begin indexingtasks thereby increasing efficiency of the system and decreasingprocessing time.

By relying on a pull-based system or asynchronous processing, the system108 can improve the load balancing or processing load across indexers206. Specifically, as indexers 206 have capacity to handle additionaltasks they can request them rather than having tasks assigned to themregardless of their backlog. Thus, indexers 206 with more resources orcapacity can process more data. This too can increase the throughput ofthe system 108.

By providing event processing and routing closer to the forwarders 204,the system 108 can reduce its reliance on third parties updating theforwarders. Instead additional processing and routing functionality canbe provided via the ingestors and/or message bus.

FIG. 2B is a block diagram of an embodiment of the data intake and querysystem 108 that includes ingestors and a message bus. In the illustratedembodiment, the data intake and query system 108 can include one or moreforwarders 204A, 204B (individually or collectively referred asforwarder 204 or forwarders 204, also referred to herein as forwardingagents) that receive data from one or more data sources 202, a searchhead 210, indexers 206A, 206B, 206C (individually or collectivelyreferred as indexer 206 or indexers 206 also referred to herein asindexing nodes), ingestors 252A, 252B (individually or collectivelyreferred as ingestor 252 or ingestors 252, also referred to herein asingestion or ingesting nodes), a message bus 254, a cluster master 262,and a shared storage system 260. It will be understood that thecomponents illustrated in FIG. 2B are for illustrative purposes only andthat the data intake and query system 108 can include fewer or morecomponents. For example, the data intake and query system 108 caninclude more or less than three indexers 206, more or less than twoingestors 252, etc. The data sources 202, forwarders 204, indexers 206,and search head 210 in the illustrated embodiment of FIG. 2B can performfunctions similar to the data sources 202, forwarders 204, indexers 206,and search head 210 described herein at least with reference to FIG. 2A.For example, one or more forwarders 204 (or forwarding agents) can beinstalled on each data sources 202, collect data from the data sources202, and forward the collected data to the indexers 206. In certainembodiments, the communications between certain components of the dataintake and query system 108 illustrated in FIG. 2A may be different fromthe communications between components of the data intake and querysystem 108 illustrated in FIG. 2B. For example, the forwarders 204 mayforward data to the ingestors 252 and the indexers 206 may receive datafrom the message bus 254.

Although FIG. 2B illustrates some example communication pathways betweenvarious components of the data intake and query system 108, it will beunderstood that the components can be configured to communicate in avariety of ways. For example, any component may be configured tocommunicate with any other component (e.g., the cluster master 262 cancommunicate with the shared storage system 260 or forwarders 204, etc.).In certain embodiments, certain components may be limited in theircommunications with other components. For example, the cluster master262 may not be communicatively coupled with the shared storage system260. As another example, the forwarders 204 may be configured tocommunicate with the data sources 202 and ingestors 252, but not theindexers 206. In a similar manner, the ingestors 252 may be configuredto communicate with the forwarders 204 and message bus 254, but not withthe indexers 206. Each of the indexers 206 may be configured tocommunicate with the search head 210, message bus 254, cluster master262, and/or shared storage system 260, but may not be configured tocommunicate with the data sources 202, forwarders 204, or ingestors 252.Further, the data intake and query system 108 can include additionalcomponents which can communicate with any one or any combination of theaforementioned components. For example, the data intake and query systemcan include a HEC or other component that forwards data to the ingestors252.

In some embodiments, some or all of the shared storage system 260, thesearch head 210, the indexers 206, the cluster master 262, and/or thecluster data store 264 may be communicatively coupled. For example, anyof the indexers 206 may be configured to individually communicate withany of the shared storage system 260, the search head 210, the clustermaster 262, and/or the cluster data store 264.

The shared storage system 260 can correspond to or be implemented ascloud storage, such as Amazon Simple Storage Service (S3) or ElasticBlock Storage (EBS), Google Cloud Storage, Microsoft Azure Storage, etc.The shared storage system 260 can be made up of one or more data storesstoring data that has been received from one or more data sources 202and/or processed by the indexers 206. The shared storage system 260 canbe configured to provide high availability, highly resilient, low lossdata storage. In some cases, to provide the high availability, highlyresilient, low loss data storage, the shared storage system 260 canstore multiple copies of the data in the same and different geographiclocations and across different types of data stores (e.g., solid state,hard drive, tape, etc.). Further, as data is received at the sharedstorage system 260 it can be automatically replicated multiple timesaccording to a replication factor to different data stores across thesame and/or different geographic locations.

Although only three indexers 206A, 206B, 206C (a first indexer 206A, asecond indexer 206B, and a third indexer 206C, individually orcollectively referred to as indexer 206 or indexers 206) and three datastores 208 are illustrated, it will be understood that the system 108can include fewer or additional indexers 206 and/or data stores 208.

In addition, it will be understood that any one or any combination ofthe aforementioned components can be removed from the system 108. Forexample, in some cases, the system 108 can be implemented withoutingestors 252. In some such cases, data from the forwarders 204 can besent to the message bus 254, and indexers 206 can retrieve the data fromthe message bus 254, as described herein. In such cases, the system 108can obtain the benefits of a pull-based system for ingesting andprocessing data, which can improve the load balancing between indexers206. As another example, in certain cases, the system e108 can beimplemented without a message bus 254. In some such cases, the ingestor252 can generate events and the indexers 206 can index the events, asdescribed herein. In such cases, the system 108 can obtain the benefitsof divorcing ingestion/event generation from event indexing. As such,the system 108 can independently scale ingestors 252 and/or indexers 206as desired. In yet other cases, the ingestors 252 and message bus 254can be omitted. In some such cases, the indexers 206 can generateevents, place the events in hot slices, roll the hot slices to warmslices and add them to an aggregate slice, and store the aggregate sliceto the shared storage system 260, as described herein. In such cases,the system 108 can obtain the benefits of creating backup copies of theevents/slices/buckets that are being processed by an indexers 206.Accordingly, it will be understood that the system 108 can be modifiedin a variety of ways and include various implementations.

2.5.1. Ingestor

The ingestors 252 (also referred to herein as ingestion nodes) can beimplemented as one or more distinct computer systems or devices and/oras one or more virtual machines, containers, PODS, or other isolatedexecution environment that is isolated from other execution environmentsof a host computing system. In some embodiments, the ingestors 252 canreceive events or data (e.g., log data, raw machine data, metrics, etc.)from a forwarder 204 or other source or component of the data intake andquery system 108 (e.g., HEC, search head, etc.), perform keywordextractions on raw data, parse raw data, generate time stamps, and/orotherwise generate events from the raw data. As such, the ingestors 252can perform certain functions that would typically be performed by theindexers 206. Accordingly, in certain embodiments in which the dataintake and query system 108 includes ingestors 252, the ingestors 252can be responsible for creating or generating events from received dataand the indexers 206 can be responsible for combining events intobuckets, indexing events in those buckets, and storing the buckets(locally and/or to the shared storage system 260). In certainembodiments that include a forwarder 204 or other component configuredto generate events and an ingestor 252, the forwarder 204 (or othercomponent) can forward the generated events to an ingestor 252 and theingestor can provide the generated events to an indexer 206 (eitherdirectly or via the message bus 254).

By including an ingestor 252, the throughput and data resiliency of thedata intake and query system can be improved. First, by having ingestors252 that can be scaled up and down independent of the indexers 206, thedata intake and query system 108 can more easily respond to increases ordecreases in data to be ingested or data to be indexed. Further a slowindexer 206 need not affect the ingestion of data from forwarders 204 orother sources. Second, by splitting up the processing tasks of theindexer 206 between the indexers 206 and the ingestors 252, the dataintake and query system 108 can increase its data resiliency given thateach component will be operating on the data for less time. Further, byhaving the message bus 254 store the events after creation but beforeindexing, the data intake and query system can reduce the amount ofprocessing required if an indexer 206 fails.

An ingestor 252 can use one or more processing pipelines, pipeline sets,buffers or queues (also referred to as producer-consumer queues), and/orcomputer processing threads to perform its functions. Each pipeline canperform one or more processing functions on data and may be implementedusing one or more processing threads. A collection of pipelines can beplaced sequentially such that the output of one pipeline can form theinput of a subsequent pipeline thereby forming a pipeline set. Thebuffers or queues can be used to temporarily maintain results of apipeline and/or be used to collect data for further processing byadditional pipelines or for communication. The buffers or queues mayalso provide some relief in the event a downstream process takes longerthan expected (e.g., processing events or communicating events to themessage bus 254 takes more time than expected).

As a non-limiting example, an ingestor 252 may include one or morepipeline sets to process incoming data. In some cases, each pipeline setcan include one or more event generation pipelines to generate eventsfrom the incoming data, a buffer or queue to temporarily store theoutput of the event generation pipelines, and one or more queue outputpipelines or workers at the output of the queue to prepare data from thequeue for communication to the message bus 254 and to communicate theprepared data to the message bus 254. In some cases, the buffer or queuecan be implemented as a producer-consumer queue to separate a read pathof the ingestor 252 (e.g., the event generation pipelines, etc.) with awrite path of the ingestor 252 (e.g., the queue output pipelines, etc.).In this way, the buffer or queue can allow for reading and writing thedata at different rates.

In some cases, the event generation pipelines can include one or moreparsing pipelines to convert incoming data into a particular format(e.g., UTF-8), perform line-breaking on the data (e.g., break up a logfile so that each line is represented by a separate pipeline dataobject), and/or extract header information (e.g., determine the host,source, and/or sourcetype of the data). In certain cases, the eventgeneration pipelines can include one or more merging pipelines to mergemultiple single lines together for events that are determined to bemulti-line events. In some cases, the event generation pipelines caninclude one or more typing pipelines to annotate the data (e.g.,indicate what punctuation is used in an event) and/or perform regexreplacement (e.g., extract a host name from the data, etc.). The outputof the event generation pipelines may be events that include raw machinedata associated with a timestamp and further associated with metadata(e.g., host, source, and sourcetype). Further the output of the eventgeneration pipelines can be placed in an output queue for furtherprocessing by one or more additional pipelines. In cases in which theingestor 252 receives pre-formed events (e.g., a forwarder 204 generatesevents from the data and communicates the events to the ingestor 252),the ingestor 252 can place the events in the output queue. In some suchembodiments, the ingestor 252 may place the events in the output queuewithout processing them using the event generation pipelines. In somesuch cases, the events may be processed by a subset of the eventgeneration pipelines depending on how much processing was done by theforwarder 204. For example, if the event was parsed and merged, but nottyped, the ingestor 252 can send the event to the typing pipeline whileskipping the parsing and merging pipelines. Accordingly, an ingestor 252can dynamically process the incoming data depending on the processingthat was performed on it by a forwarder 204 or other component. Incertain cases, the ingestor 252 can dynamically process the incomingdata based on routing keys or identifiers in the received data or inmetadata associated with the data that is to be processing. The routingkeys or identifiers can indicate what processing has already been doneon the data.

The output queue pipelines or worker can be used to group events fromthe queue together and/or encode the grouped events. In certain cases,the grouped events can be encoded using protobuf, thrift, S2S, otherschema-based encoding, or other encoding devices, mechanisms, oralgorithms. The grouped events can be sent to the message bus as amessage payload. In certain cases, the ingestor 252 can group only wholeevents. In other words, the ingestor 252 may not split an event betweenmultiple groups. As such, the size of a group of events canincrease/decrease by one whole event. In certain cases, the ingestor 252can split up parts of an event across multiple groups of events.

In some cases, the queue output pipelines or worker can also determinewhether the grouped events are to be sent to the message queue 256 orthe data store 258 of the message bus 254. In certain cases, theingestor 252 can determine the size of the group events. Depending onthe size of the grouped events, the ingestor 252 can send the groupedevents to the message queue 256 or the data store 258 of the message bus254. For example, if the grouped events satisfy or are larger than amessage size threshold, the queue output pipelines or worker can sendthe grouped events to the data store 258, obtain a location reference ofthe grouped events in the data store 258, and send the locationreference to the message queue 256. If the grouped events do not satisfyor are smaller than or equal to the message size threshold, the queueoutput pipelines or worker can send the grouped events to the messagequeue 256. In determining whether the grouped events satisfy the messagesize threshold, the queue output pipelines or worker can compare themessage size threshold with the size of the grouped events withoutmodification and/or compare the size of the grouped events after theyare encoded. Similarly, in communicating the grouped events to themessage bus 254, the queue output pipeline or worker can send thegrouped events without modification and/or encode them and send anencoded version of the grouped events. The message size threshold can bebased on size limits of a message as determined by the capacity orcapabilities of the message bus 254 or message queue 256. In some cases,the message queue 256 may be external to or remote from the ingestors252 and/or indexers 206 and may developed by a third party. As such, themessage queue 256 may therefore have certain characteristics, capacityor limitations with regard to the size of messages that it can process.Accordingly, in some such cases, the message size threshold can be basedon the capacity and/or capabilities of the message queue 256.

It will be understood that the pipelines described herein are forexample purposes only and that each pipeline can perform fewer or morefunctions and that a pipeline set can include fewer or more pipelines.For example, additional pipelines or the pipelines described above canbe used to extract or interpolate a timestamp for events, determineand/or associate event with metadata (e.g., host, source, sourcetype),encode a group of events, etc. Accordingly, it will be understood thatany one or any combination of the functions described above can begenerally understood as being performed by an ingestor 252. For example,it will be understood that an ingestor 252 can receive input data,dynamically process the input data depending on what processing the datahas already undergone, generate events from the input data, group eventsto form grouped events, and communicate the grouped events to themessage bus 254. In communicating the grouped events to the message bus,the ingestor 252 can send the grouped events to the message queue 256 orsend the grouped events to the data store 258 and send a locationreference to the grouped events in the data store to the message queue256.

The ingestor 252 or a monitoring component, such as the cluster master262, can monitor or track the relationship between received data (or adata chunk), generated events, event groups, and message payload (e.g.,which events were generated from which data and to which event groupswere the events added and to what message the event groups correspond).For example, when a data chunk is received at the ingestor 252, theingestor 252 can track which events were generated from that data chunk,the event groups to which the events were added, and the messages ormessage payloads that included the events. Accordingly, once a messagepayload or group of events has been stored in the message bus 254, theingestor 252 can identify which events have been stored, and how manyevents that were generated from a particular data chunk received from aparticular source have been stored to the message bus 254. As such, onceall of the events generated from a particular data chunk have been savedto the message bus 254, the ingestor 252 can send an acknowledgement tothe source of the data chunk, such as a forwarder 204, HEC, etc. Basedon the received acknowledgement the source can delete the data chunkfrom any buffers, queues, or data stores that it has and/or send anacknowledgement to a data source 202, so that the data source 202 candelete the data chunk.

In some cases, the cluster master 262 or other monitoring component canmonitor the amount of data being processed by the ingestors 252 and/orthe capacity of the ingestors 252. For example, each ingestor 252 cansend the monitoring component various metrics, such as, but not limitedto, CPU usage, memory use, error rate, network bandwidth, networkthroughput, bytes uploaded to the message bus 254 or message queue 256,time taken to encode the data, time taken to schedule and execute a jobor pipeline, etc. Based on the information from the ingestors 252, themonitoring component can terminate one or more ingestors 252 (e.g., ifthe utilization rate of an ingestor 252 or the ingestors 252 satisfies alow utilization threshold, such as a 20% utilization or 20% utilizationfor ten consecutive minutes, etc.) and/or instantiate one or moreadditional ingestors 252 (e.g., if the utilization rate of the aningestor 252 or the ingestors 252 satisfies a high utilizationthreshold, such as 90% utilization or 90% utilization for tenconsecutive minutes). Any one or any combination of the aforementionedmetrics can be used to determine whether to terminate or instantiate oneor more ingestors 252. In some cases, the monitoring component canmonitor an individual ingestor 252 to determine whether the individualingestor 252 should complete the processing of the data that has beenassigned to it and shut down or whether to instantiate an additionalingestor 252.

In some cases, the monitoring component can instantiate one or moreadditional ingestors 252 based on a frequency at which messages areplaced on the message queue 256 or the amount of messages placed on themessage queue. For example, if the frequency or amount of messagessatisfies or falls below a frequency or amount threshold, this couldmean that the ingestors 252 do not have sufficient capacity to processdata and generate message payloads in a timely manner. In some suchcases, the monitoring component can instantiate one or more additionalingestors 252 to improve throughput. As another scenario, if an amountof data being sent to the ingestors 252 satisfies an amount threshold orincreases, then depending on the number of ingestors 252 instantiated,additional ingestors 252 can be instantiated. In a similar way, if theamount of data being sent to the ingestors 252 increases by a thresholdamount, then additional ingestors 252 can be instantiated.

In certain cases, each individual ingestor 252 can be its own monitoringcomponent (or monitor other ingestors 252) to determine whether itsatisfies a low utilization threshold and should complete its processing(e.g., finish converting data into events, grouping the events, andsending the groups of events to the message bus 254) and shut down orwhether it satisfies a high utilization threshold and should requestthat an additional ingestor 252 be instantiated.

In any case, increasing (creating/instantiating) or decreasing(terminating/shutting down) the number or quantity of ingestors 252 canbe done dynamically and can be independent of the number of indexers 206that are indexing data. In this way, there can be fewer or morecomponents ingesting data (e.g., ingesting nodes) and creating eventsthan components (e.g., indexing nodes) that are grouping events to formbuckets and storing the buckets. Furthermore, by dynamically andindependently scaling ingestors 252, the data intake and query system108 can improve the data ingestion throughput and react to data surgesor declines in a performant way. In addition, the data intake and querysystem can independently and separately react to too little or too muchingestion capacity and/or indexing capacity.

2.5.2. Message Bus

The message bus 254 can include a message queue 256 and/or a data store258. In certain cases, the message queue 256 may be remotely locatedfrom the ingestors 252 and/or the indexers 206. In some cases, themessage queue 256 can be a cloud-based message queue 256 that isinstantiated in a cloud environment or shared resource environment orcan be an on-prem message queue 256 that is instantiated in a non-sharedresource environment.

The message queue 256 can operate according to a publish-subscribe(“pub-sub”) message model. In accordance with the pub-sub model, dataingested into the data intake and query system 108 may be atomized as“messages,” each of which is categorized into one or more “topics.” Themessage queue 256 can maintain a queue for each such topic, and enabledevices to “subscribe” to a given topic. As messages are published tothe topic, the message queue 256 can function to transmit the messagesto each subscriber, and ensure message resiliency until at least eachsubscriber has acknowledged receipt of the message (e.g., at which pointthe message queue 256 may delete the message). In this manner, themessage queue 256 may function as a “broker” within the pub-sub model. Avariety of techniques to ensure resiliency at a pub-sub broker are knownin the art, and thus will not be described in detail herein. In oneembodiment, a message queue 256 is implemented by a streaming datasource. As noted above, examples of streaming data sources include (butare not limited to) Amazon's Simple Queue Service (“SQS”) or Kinesis™services, devices executing Apache Kafka™ or Pulsar software, or devicesimplementing the Message Queue Telemetry Transport (MQTT) protocol. Anyone or more of these example streaming data sources may be utilized toimplement a message queue 256 in accordance with embodiments of thepresent disclosure.

In some cases, the message queue 256 sends messages in response to arequest by a subscriber. In some such cases, the message queue 256 cansend a message in response to a request by an indexer 206. In responseto the request, the message queue 256 can provide the message to theindexer 206. In some cases, and indexer 206 may request multiplemessages simultaneously or concurrently. In some such cases, the messagequeue 256 can respond with the number of messages requested.

In certain cases, the message queue 256 can retain messages until theyhave been acknowledged by a subscriber. For example, after sending amessage to an indexer 206, the message queue 256 can retain the messageuntil it receives and acknowledgement from the indexer 206. If themessage references data (e.g., grouped events) in the data store 258,then the data in the data store 258 can be deleted along with themessage in the message queue 256. As described herein, in some cases themessage queue 256 can receive an acknowledgment from an indexer 206after the indexer 206 has stored all the events associated with aparticular message (e.g., events in the message or events referenced bythe message that are stored in the data store 258) in the shared storagesystem 260 (as part of a slice and/or as part of a bucket). In responseto receiving the acknowledgement, the message queue 256 can delete themessage and/or relevant events from the message queue 256 and/or datastore 258.

The data store 258 can be implemented as a separate computing deviceand/or as a cloud-based data store as part of a cloud storage, such as,but not limited to, Amazon Simple Storage Service (S3) or Elastic BlockStorage (EBS), Google Cloud Storage, Microsoft Azure Storage, etc. Incertain cases, the data store 258 can be implemented as an object store.In some cases, the data store 258 can form part of the shared storagesystem 260, e.g., as a separately accessible data store of the sharedstorage system 260 and/or as a separate instance of cloud storage. Thedata store 258 can be configured to provide high availability, highlyresilient, low loss data storage. In some cases, to provide the highavailability, highly resilient, low loss data storage, the data store258 can store multiple copies of the data in the same and differentgeographic locations and across different types of data stores (e.g.,solid state, hard drive, tape, etc.). Further, as data is received atthe data store 258 it can be automatically replicated multiple timesaccording to a replication factor to different data stores across thesame and/or different geographic locations.

The data store 258 can be used to store larger messages or larger groupsof events received from the ingestors 252. In some cases, the size of amessage or size of the group of events (in the aggregate) may exceed amessage size limit of the message queue 256. For example, the messagequeue 256 may only have capacity for or be configured to processmessages that are no larger than 256 kb. If the group of events (ormessage payload) for a message exceeds that size alone or in combinationwith other message data (e.g., a message header) then the ingestor 252can store the group of events (or message payload) to the data store 258and obtain a location reference to the group of events. The ingestor 252can send the location reference to the message queue 256.

On the indexer side, upon downloading, requesting, or receiving amessage with a location reference as the message payload, the indexer206 can use the location reference to obtain the relevant events fromthe data store 258 (as a second message payload). In certain cases, theingestor 252 determines whether the group of events exceeds the messagesize after it has encoded the group of events. In some cases, theingestor 252 determines whether the group of events exceeds the messagesize after before or without encoding the group of events. It will beunderstood that the size 256 kb is a non-limiting example and that theingestors 252 can be configured to use any data size as a message sizethreshold. Accordingly, an ingestor 252 can store groups of events thatsatisfy or exceed the message size threshold to the data store 258,obtain a location reference of the groups of events stored in the datastore 258, and send the location reference to the message queue 256 forinclusion as part of a message (e.g., as the message payload).

2.5.3. Indexers

As described herein, an indexer 206 can be the primary indexingexecution engine, and can be implemented as a distinct computing device,virtual machine, container, etc. For example, the indexers 206 can betasked with parsing, processing, indexing, and/or storing the datareceived from the forwarders 204. Specifically, in some embodiments, theindexer 206 can parse the incoming data to identify timestamps, generateevents from the incoming data, group and save events into buckets,generate summaries or indexes (e.g., time series index, inverted index,keyword index, etc.) of the events in the buckets, and store the bucketslocally (for example, in the data store 208) and/or in shared storagesystem 216. In addition, as described herein, the indexers 206 can beused to search data. In embodiments where indexers 206 search data, they(or the component that does search data) may be referred to as “searchpeers” or “search nodes.” Accordingly, reference to a search peer orsearch node can refer to an indexer 206 or other component or computingdevice configured to perform one or more search-related tasks.

When an indexer 206 finishes processing or editing a bucket, it canstore the bucket locally and/or to the shared storage system 260. Asdescribed herein, the buckets that are being edited by an indexer 206can be referred to as hot buckets or editable buckets. For example, anindexer 206 can add data, events, and indexes to editable buckets in thedata store 208, etc. Buckets in the data store 208 that are no longeredited by an indexer 206 can be referred to as warm buckets ornon-editable buckets.

In some cases, such as where the data intake and query system 108includes ingestors 252, the indexers' 206 processing tasks can bereduced. For example, as described herein, the ingestors 252 can be usedto generate events from incoming data. In some such cases, the indexers206 may not generate events, but may still group events (in buckets) forstorage and searching. As part of grouping the events for storage andsearching, the indexers 206 can group events by associated indexes. Asdescribed herein, the indexes may be user defined and applied to eventsfrom a particular source or host, or events having a particularsourcetype, or events received during a particular time window. In anycase, an indexer 206 can determine to what index events are associatedand group the events by index. Further, the indexer 206 can createbuckets and slices for each index. The buckets and slices can be usedfor storing and searching events. In some cases, one or more slices canbe used to form part of a bucket.

The indexer 206 can determine the amount of data that it will process.To do this, the indexer 206 can monitor its capacity for processingadditional data. For example, the indexer 206 can monitor its CPU usage,memory use, error rate, network bandwidth, network throughput, timetaken to process the data, time taken to schedule and execute a job orpipeline, the number of events, slices, and buckets that it is currentlyprocessing, time to download a message, time to decode a message, timeto purge a message or send an acknowledgement, and/or time to renewmessages if used or needed and amount of processing resources that itanticipates would be needed to process additional events. If the indexer206 determines that it has sufficient resources to process additionalevents, it can request another message from the message queue 256. Inresponse, the message queue 256 can provide the indexer 206 with amessage.

Upon receipt of a message from the message queue 256, the indexer 206can process the message. This can include decoding encoded eventsassociated with the message, sorting the events (e.g., by index),storing the events in slices and buckets, etc. In cases where themessage includes a reference to grouped events in the data store 258,processing the message can include retrieving the grouped events fromthe data store 258.

In certain cases, an indexer 206 can assign each event to a (hot) bucketand a (hot) slice. In some cases, the indexer 206 assigns the event to abucket based on the index with which the event is associated and assignsthe event to a slice based on the assigned bucket or index to which theevent is associated. In some such cases, the indexer 206 can include atleast one hot slice for each bucket and least one hot bucket for eachindex for which the indexer 206 is processing events. For example, ifthe indexer 206 is processing events associated with a main index, testindex, and devops index, the indexer 206 can include three hot bucketsassociated with each of the indexes, respectively, and at least threehot slices associated with each of the three buckets, respectively(e.g., a main hot slice and main hot bucket, a test hot slice and testhot bucket, and a devops hot slice and devops hot bucket). In addition,the indexer 206 may include one or more warm slices and/or aggregateslices and one or more warm buckets for each index for which the indexer206 is processing events. With continued reference to the example above,the indexer 206 may include six test warm slices as part of two testaggregate slices, three test warm buckets, five main warm slices as partof one main aggregate slices, seven main warm buckets, one devops warmslice as part of one devops aggregate slice, and one devops warm bucket.

Further, if the indexer 206 receives an event associated with an indexfor which there is no editable bucket or editable slice, the indexer 206can generate an editable bucket or editable slice, as the case may be,and assign the event to the newly generated editable bucket or editableslice.

Based on a slice rollover policy, the indexer 206 can convert a hot oreditable slice (slice to which events are being actively added) to awarm or non-editable slice and add it an aggregate slice. The aggregateslice can include one or more warm slices associated with the samebucket. The slice rollover policy can include any one or any combinationof a hot slice size threshold, hot slice timing threshold, or otherthreshold. The thresholds can be user specified or based on processingcharacteristics of the indexer 206 or shared storage system 260 or othercomponent of the data intake and query system 108. In some cases, once ahot slice size threshold (e.g., 1 MB) or hot slice timing threshold(e.g., 30 seconds) is satisfied or exceeded, the indexer 206 can convertthe hot slice to a warm or non-editable slice and add it to an aggregateslice. In certain cases, before adding the warm slice to the aggregateslice, the indexer 206 can compress the warm slice, thereby reducing theamount of memory and disk space used to store the warm slice. When a hotslice becomes warm or non-editable, the indexer 206 can generate a newhot slice, begin filling it with events, and roll it to the aggregateslice based on the slice rollover policy in due course, etc. In thisway, the indexer 206 can maintain a hot slice for accepting new eventsas they are received.

As described herein, in some cases, the indexers 206 can store a copy ofdata that it is processing (e.g., slices of data corresponding to a hotbucket) and/or a copy of the results of processing/indexing the data(e.g., warm buckets) in the shared storage system 260. Based on anaggregate slice backup policy, the indexer 206 can store the aggregateslices to the shared storage system 260. The aggregate slice backuppolicy can include any one or any combination of an aggregate slice sizethreshold, aggregate slice timing threshold, etc. The thresholds can beuser specified or based on processing characteristics of the indexer206, shared storage system 260, or other component of the data intakeand query system. In some cases, once an aggregate slice size threshold(e.g., 10 MB) or aggregate slice timing threshold (e.g., 2 minutes) issatisfied or exceeded, the indexer 206 can flag or mark the aggregateslice for copying to the shared storage system 260 and/or copy theaggregate slice to the shared storage system 260.

In addition, in some cases, the aggregate slice backup policy canindicate how the aggregate slices are to be process and/or stored. Forexample, the aggregate slice backup policy can indicate that theaggregate slice is to be compressed prior to storage. By compressing theaggregate slice, the indexer 206 can reduce the amount of memory and/ordisk space used to store the aggregate slice.

In certain cases, the aggregate slice backup policy can indicate thatthe slices of the aggregate slice are to be uploaded in data offset orlogical offset order. For example, if the aggregate slice includes afirst slice from the logical offset 0-1000, a second slice from logicaloffset 1001-2500, and a third slice from logical offset 2501-3600, theaggregate slice backup policy can indicate that the first slice is to beuploaded, stored, and acknowledged by the shared storage system 260before beginning the upload of the second slice, and so on. In this way,if there are any issues with uploading the slices, the indexer 260 canprovide a guarantee that if the third slice was uploaded then the firstand second slices should also exist in the shared storage system 260. Assuch, in the event a restore is started (e.g., because the indexer 206failed), the system can determine which slices are available to restorethe lost data or bucket.

In certain cases, prior to copying an aggregate slice to the sharedstorage system 260, the indexer 206 can verify whether the bucketassociated with the aggregate slice is being uploaded or has alreadybeen upload to the shared storage system 260. If the correspondingbucket is being uploaded or has already been uploaded, the indexer 206may decide not to store the aggregate slice to the shared storage system260 given that the corresponding bucket that is stored in the sharedstorage system 260 includes a copy of the data in the aggregate slice.

Upon storing the aggregate slices to the shared storage system 260, theindexer 206 can notify the message bus 254. In some cases, the indexer206, or other monitoring component, such as the cluster master 262,tracks which events came from which messages of the message bus. Onceall of the events from a particular message have been copied to theshared storage system 260, the indexer 206 (or other monitoringcomponent) can inform the message bus 254. In some cases, as each eventof a message is stored to the shared storage system 260, the indexer 206(or monitoring component) can inform the message bus 254. In eithercase, once all the events from a message are stored in the sharedstorage system 260 (either as part of an aggregate slice or as part of abucket), the message bus 254 can purge the relevant message and eventsfrom the message queue 256 and data store 258.

By storing the aggregate slices to the shared storage system 260, theindexer 206 can improve the data availability and resiliency of the dataintake and query system 108. For example, if the indexer 206A fails orbecomes unavailable, another indexer 206B can be assigned to process theslices in the shared storage system 260 to form a bucket. As anotherexample, if the indexer 206A is responsible for searching an aggregateslice as part of a search query but is unavailable, another indexer 206,such as indexer 206B, can be assigned to download the aggregate slicefrom the shared storage system 260 and search the aggregate slice. Incertain cases, before searching the aggregate slice, the indexer 206Bcan use it to rebuild a corresponding bucket. For example, if theindexer 206A failed before the bucket corresponding to the aggregateslice was uploaded to the shared storage system 260 (or if only parts ofthe bucket, like the aggregate slices, were uploaded to the sharedstorage system 260), the indexer 206B can rebuild that bucket using theaggregate slice and then search the rebuilt bucket as part of thesearch.

Concurrent to storing aggregate slices to the shared storage system 260,the indexer 206 can generate buckets that include the events of theaggregate slices. In some cases, a bucket can include one or moreaggregate slices or include events that can be found in one or moreaggregate slices. Accordingly, as aggregate slices are copied to theshared storage system 260, the original aggregate slice (or the eventscontained therein) may remain as part of a hot bucket at the indexer206.

Based on a bucket rollover policy, the indexer 206 can convert a hot oreditable bucket to a warm or non-editable bucket. The bucket rolloverpolicy can include any one or any combination of bucket size threshold,bucket timing threshold, or other threshold. The thresholds can be userspecified or based on processing characteristics of the indexer 206,shared storage system 260 or other component of the data intake andquery system 108. In some cases, once a bucket size threshold (e.g., 750MB) or bucket timing threshold (e.g., 10 minutes) is satisfied orexceeded, the indexer 206 can convert the hot bucket to a warm bucketand store a copy of the warm bucket in the shared storage system 260. Insome cases as part of storing the copy of the warm bucket to the sharedstorage system 260, the indexer 206 can mark or flag the warm bucket forupload. In certain cases, the indexer 206 can use the flag or marking toidentify associated aggregate slices and/or hot slices that are not tobe upload or are to be deleted. When a hot bucket is converted to a warmbucket or non-editable bucket, the indexer 206 can generate a new hotbucket, begin filling it with events, and roll it on the bucket rolloverpolicy in due course, etc. In this way, the indexer 206 can maintain ahot bucket for accepting new events (for a particular index) as they arereceived.

After storing a copy of the warm bucket to the shared storage system260, aggregate slices that are associated with the copied bucket andstored in the shared storage system 260 can be deleted. As describedherein, the aggregate slices associated with a bucket include the eventsof the bucket. When a warm bucket is copied to the shared storage system260, the aggregate slices (and events) are copied as part of the bucketalong with other bucket-related information and files (e.g., invertedindexes, metadata, etc.). Accordingly, once a copy of a warm bucket isstored in the shared storage system 260, aggregate slices stored in theshared storage system 260 before the warm bucket was copied includeduplicate data and can be deleted (e.g., by the cluster master 262,shared storage system 260, and/or the indexer 206). In addition, theindexer 206 can delete any hot slices or aggregate slices associatedwith the rolled warm bucket that remain on the indexer 206.

By storing a copy of the warm to the shared storage system 260, theindexer 206 can improve the data availability and resiliency of the dataintake and query system 108. For example, if the indexer 206 fails orbecomes unavailable to search a bucket that it stored to the sharedstorage system 260 or is otherwise responsible for searching, anotherindexer 206 can be assigned to search the bucket.

As described herein, a monitoring component, such as the cluster master262 can track the copies of the data (e.g., aggregate slices and/orbuckets) stored in the shared storage system 260. In the event a firstindexer 206 fails during indexing or search, the monitoring componentcan assign a second indexer 206 to index or search the data that hadbeen assigned to the first indexer 206 for indexing and/or searching,respectively. In this way, the cluster master 262 and shared storagesystem 260 can improve the data availability and resiliency of the dataintake and query system 108.

In some embodiments, once the slices of data or warm buckets are copiedto the shared storage system 260, an indexer 206 can notify a monitoringcomponent, such as the cluster master 262, that the data associated withthe hot or warm bucket has been stored. In addition, the indexer 206 canprovide the monitoring component with information about the bucketsstored in the shared storage system 260, such as, but not limited to,location information, index identifier, time range, etc. As describedherein, the cluster master 262 can use this information to update thecluster data store 264. In certain embodiments, the indexer 206 canupdate the cluster data store 264. For example, the indexer 206 canupdate the cluster data store 264 based on the information it receivesfrom the shared storage system 260 about the stored buckets.

The indexer 206 or a monitoring component, such as the cluster master262, can monitor or track the relationship between received data(messages or message payload), events, hot/warm slices, aggregateslices, and buckets (e.g., which events came from which message ormessage payload and to which hot/warm slice, aggregate slice, and bucketwere the events added). For example, when a message or message payloadis received at the indexer 206, the indexer 206 can track which eventswere extracted from message payload, the hot/warm slice to which theevents were added, the aggregate slice to which the hot/warm slice wasadded, and the bucket associated with or that includes the aggregateslice, etc. Accordingly, once an aggregate slice or bucket has beencopied to the shared storage system 260, the indexer 206 can identifywhich events have been stored, and how many events that were extractedfrom a particular message received from the message bus 254 have beenstored to the shared storage system 260. As such, once all of the eventsfrom a particular message have been saved to the shared storage system260, the indexer 206 can send an acknowledgement to the message bus 254.Based on the received acknowledgement the message bus 254 can delete themessage and associated events from the message queue 256 and/or datastore 258.

Accordingly, in some cases, each event can be twice acknowledged as partof the ingestion and indexing process. Specifically, a firstacknowledgement can indicate that an event has been generated and storedin the message bus 254 and that responsibility for ensuring theavailability has passed to the message bus 254. A second acknowledgementcan indicate that the event has been added to a bucket and/or aggregateslice and is stored in the shared storage system 260, and thatresponsibility for ensuring the availability has passed to the sharedstorage system 260. By using a dual acknowledgement, the data intake andquery system 108 can increase throughput and data resiliency. Throughputand resiliency can be increased given that the amount of time that aparticular component (other than the shared storage system 260) retainsresponsibility for a particular event is decreased. For example, ratherthan a forwarder 204 having to wait until an event is fully processedand stored before deleting a local copy of the data corresponding to theevent, it can wait for the first acknowledgement indicating that theevent has been stored in the message bus 254. As such, the componentscan more quickly delete copies of the particular event, thereby freeingup space for additional events. This can be especially be helpful wherean indexer 206 fails during processing of an event. In such a scenario,the entire data pipeline from the forwarder 204 to the indexer is notdelayed or backed up, and the forwarder 204 can continue to send data toan ingestor 252 for processing given that the failure of the indexer 206does not affect a forwarder's output buffer or the ability of theforwarder 204 to forward data and receive acknowledgements for the data.

In some cases, the cluster master 262 or other monitoring component canmonitor the amount of data being processed by the indexers 206 and/orthe capacity of the indexers 206. For example, each indexer 206 can sendthe monitoring component various metrics, such as, but not limited to,CPUCPU usage, memory use, error rate, network bandwidth, networkthroughput, time taken to process the data, time taken to schedule andexecute a job or pipeline, the number of events, slices, and bucketsthat it is currently processing, time to download a message, time todecode a message, time to purge a message or send an acknowledgement,and/or time to renew messages if used or needed, etc. Based on theinformation from the indexers 206, the monitoring component canterminate one or more indexers 206 (e.g., if the utilization rate of anindexer 206 or the indexers 206 satisfies a low utilization threshold,such as a 20% utilization or 20% utilization for ten consecutiveminutes, etc.) and/or instantiate one or more additional indexers 206(e.g., if the utilization rate of the an indexer 206 or the indexers 206satisfies a high utilization threshold, such as 90% utilization or 90%utilization for ten consecutive minutes). In some cases, the monitoringcomponent can monitor an individual indexer 206 to determine whether theindividual indexer 206 should complete the processing of the data thathas been assigned to it and shut down or whether to instantiate anadditional indexer 206. In some cases, the monitoring component caninstantiate one or more additional indexers 206 based on a frequency atwhich messages are requested from the message queue 256 or the amount ofmessages requested from the message queue. For example, if the frequencyor amount of requests satisfies or falls below a frequency or amountthreshold, this could mean that the indexers 206 do not have sufficientcapacity to process messages in a timely manner. In some such cases, themonitoring component can instantiate one or more additional indexers206.

In certain cases, each individual indexer 206 can be its own monitoringcomponent (or monitor other indexers 206) to determine whether itsatisfies a low utilization threshold and should complete its processing(e.g., assigning events it has to hots/warm slices, assigning warmslices to aggregate slices, storing aggregate slices to the sharedstorage system 260, and storing relevant buckets to the shared storagesystem 260) and shut down or whether it satisfies a high utilizationthreshold and should request that an additional indexer 206 beinstantiated.

In any case, increasing (creating/instantiating) or decreasing(terminating/shutting down) the number or quantity of indexers 206 canbe done dynamically and can be independent of the number of ingestors252 that are ingesting data and generating events. In this way, therecan be fewer or more components indexing data (e.g., indexing nodes) andgenerating slices, aggregate slices, and buckets than components (e.g.,ingesting nodes) that are creating events. Furthermore, by dynamicallyand independently scaling indexers 206, the data intake and query system108 can improve the data indexing throughput and react to data surges ordeclines in a performant way. In addition, the data intake and querysystem can independently and separately react to too little or too muchingestion capacity and/or indexing capacity.

2.3.4. Cluster Master

The cluster master 262 can be used to track processing, storage, andsearching assignments within the data intake and query system 108. Inthe event of a failed indexer 206, the cluster master 262 can be used toreassign tasks within the data intake and query system 108.

In some cases, the cluster master 262 can maintain the cluster datastore 264 with information identifying groups of data (e.g., slices ofdata, hot buckets, warm buckets, etc.), the location of the groups ofdata, as well as an identification of the indexers assigned to process,store, and/or search the groups of data. Accordingly, the cluster master262 can be communicatively coupled to one or more components of the dataintake and query system 108, such as any combination of one or more ofthe indexers 206, the search head 210, the shared storage system 260,and/or the cluster data store 264. The cluster master 262 can monitorindexers 206, determine data-indexer 206 assignment (e.g., whichindexers 206 are to process or search which data), update data-indexerassignments, receive filter criteria associated with queries, identifybucket map identifiers (described below), receive requests for andcommunicate bucket map identifiers, identify and communicate dataidentifiers associated with bucket map identifiers, etc.

As mentioned, the cluster master 262 can maintain the cluster data store264. For example, the cluster master 262 can communicate with or monitorthe indexers 206 to determine, identify, or update information, such asindexer (or search peer) identifiers, data identifiers (e.g., bucket orslice identifiers), location information, metrics, status identifiers,indexer assignments, or bucket map identifiers that can be used to aidin the processing, storage, or search of data. As another example, thecluster master 262 can communicate with or monitor the search head 210to determine, identify, or update information, such as filter criteria,indexer identifiers, or bucket map identifiers to aid in the search ofdata. The cluster master 262 can populate the cluster data store 264and/or update it over time with the data that it determines from theindexers 206 and/or search head 210. For example, as informationchanges, the cluster master 262 can update the cluster data store 264.In this way, the cluster data store 264 can retain an up-to-datedatabase of data-indexer information.

In some cases, the cluster master 262 can maintain the cluster datastore 264 by pinging the indexers 206 for information or passivelyreceiving it based on the indexers 206 independently reporting theinformation. For instance, the cluster master 262 can ping or receiveinformation from the indexers 206 at predetermined intervals of time,such as every X number of seconds, or every X minute(s), etc. Inaddition or alternatively, the indexers 206 can be configured toautomatically send their data to the cluster master 262 and/or thecluster master 262 can ping a particular indexer 206 after the passageof a predetermined period of time (for example, every X number ofseconds or every X minutes) since the cluster master 262 requestedand/or received data from that particular indexer 206.

In some cases, the cluster master 262 can maintain the cluster datastore 264 by receiving status update communications from the indexers206. Status update communications or “heartbeats” can occur periodicallyor according to a schedule, policy, or algorithm. For example, atime-based schedule may be used so that heartbeats may be performedevery X number of seconds, or every X minute(s), and so forth. In somecases, the cluster master 262 can determine that an indexer 206 isunavailable, failing, or that an indexer did not process assigned databased on the status update communications or absence of status updatecommunications from the indexer 206, and can update the cluster datastore 264 accordingly.

2.5.5. Cluster Data Store

The cluster data store 264 can store information relating to groups ofdata that are stored and/or processed by the data intake and querysystem 108. In some embodiments, this information can include bucket mapidentifiers, indexer identifiers, metrics, status identifiers, orindexer assignments. The cluster data store 264 can be maintained (forexample, populated, updated, etc.) by the cluster master 262. Asmentioned, in some embodiments, the cluster master 262 and cluster datastore 264 can be separate or independent of the indexers 206.Furthermore, in some cases, the cluster data store 264 can be separatefrom or included in, or part of, the cluster master 262.

As described herein, a bucket map identifier can be associated withfilter criteria, groups of data that satisfy the filter criteria, and/orindexers 206 assigned to process and/or search the identified groups ofdata. In some cases, the cluster data store 264 can store one or morebucket map identifiers. In some embodiments, the bucket map identifierscan be implemented as alphanumeric identifiers or other identifiers thatcan be used to uniquely identify one bucket map identifier from anotherbucket map identifier stored in the cluster data store 264.

In some cases, each bucket map identifier can identify, filter criteria,groups of data that satisfy the filter criteria, and/or indexers 206assigned to process and/or search the identified groups of data. Thefilter criteria can include one or more criterion used to filter data aspart of a search or to otherwise identify groups of data. For example,the filter criteria can include an index identifier and/or time range.In certain embodiments, the filter criteria can include, one or morefield-value pairs, metadata (e.g., host, source, sourcetype), or othercriterion that can be used to filter data as part of a search.

In addition, each bucket map identifier can be associated with one ormore groups of data. A group of data can include pre- and/or postprocessed data. In some cases, a group of data can correspond to one ormore hot buckets and/or warm buckets. In some cases, a group of data caninclude a set of one or more slices of data before it is processed by anindexer 206 (e.g., slices of a hot bucket). In some cases, a group ofdata can include a bucket or the content of a bucket, such as one ormore files that include a group of events generated from one or moreslices of data, an inverted index corresponding to the events, etc. Incertain embodiments, the bucket map identifier can be associated to thegroups of data using one or more data identifiers. For example, thecluster master 262 can associate one or more data identifierscorresponding to the groups of data with a particular bucket mapidentifier.

A non-limiting example of a data structure for storing the bucket mapidentifier is illustrated in Table 1.

TABLE 1 Bucket Map Data Identifier Filter Criteria Identifiers IndexerID 65 Index: main B56, B67, A423, 2226, Time Range: B89, B92, B603 ET:2020-01-01 00:00:00 Slice4 LT: 2020-01-02 00:00:00

In the illustrated embodiment, the bucket map identifier 65 isassociated with filter criteria that includes an index identifier“main,” and a time range from “2020-01-01 00:00:00” to “2020-01-0200:00:00,” data identifiers B1, B6, B8, Slice4, and indexer or searchpeer identifiers A423, 2226, B603. Based on the above example, thegroups of data associated with the data identifiers B56, B67, B89, B92,and Slice4 are part of the index “main,” are at least partially withinthe time range from “2020-01-01 00:00:00” to “2020-01-02 00:00:00,” andcan be accessed using indexers/search peers A423, 2226, B603. It will beunderstood that the bucket map identifier entries can be configured in avariety of ways. It will be understood that the bucket map identifierdata structure can include fewer or more information. For example,additional filter criteria can be included or indexer identifiers may beomitted, etc. In some cases, a bucket map identifier may include anidentification of the specific indexer that is to search a particulargroup of data. With reference to the example in Table 1, the bucket mapidentifier entry may indicate that the data associated data identifierB56 and B67 is to be searched by indexer A423, the data associated withdata identifiers B89 is to be searched by the indexer 2226, and/or thedata associated data identifiers B92 and Slice4 is to be searched byindexer B603, etc. In certain embodiments, the bucket map identifier maynot include reference to or be associated with unprocessed/unindexed orpartially processed/indexed data, such as hot buckets or slices of data(e.g., Slice4), and/or may only include references to data that has beenindexed/processed, such as warm buckets.

In some cases, the bucket map identifiers may not be directly associatedwith indexer identifiers (e.g., the bucket map identifier data structureshown in Table 1 may not include indexer identifiers). In some suchembodiments a separate data structure may associate individual dataidentifiers with indexer identifiers. For example, Table 2 illustratesan example data indexer assignment data structure.

TABLE 2 Indexer ID Data Identifiers A423 B2, B6, B8, B50, B51, B54, B56,B59, B63, B66, B67, Slice1, Slice3, Slice20 2226 B3, B5, B9, B40, B42,B43, B44, B48, B70, B73, B89, Slice2, Slice10 B603 B1, B7, B10, B13,B15, B18, B75, B90, B92, Slice4, Slice5, Slice7, Slice8, Slice9

In the illustrated embodiment, the indexer identifier “A423” isassociated with the data identifiers B2, B6, B8, B50, B51, B54, B56,B59, B63, B66, B67 corresponding to eleven buckets and data identifiersSlice1, Slice3, Slice20 corresponding to three slices of data, theindexer identifier “2226” is associated with the data identifiers B3,B5, B9, B40, B42, B43, B44, B48, B70, B73, B89, corresponding to elevenbuckets and data identifiers Slice2, Slice10, Slice4 corresponding tothree slices of data, and the indexer identifier “B603” is associatedwith the data identifiers B1, B7, B10, B13, B15, B18, B75, B90, B92corresponding to nine buckets and data identifiers Slice5, Slice7,Slice8, Slice9 corresponding to four slices of data. Based on the aboveexample, the indexer A423 is assigned to search buckets of datacorresponding to data identifiers B56 and B67, the indexer 2226 isassigned to search a bucket of data corresponding to data identifierB89, and the indexer B603 is assigned to search buckets of datacorresponding to data identifier B92 and a slice of data correspondingto data identifier Slice4.

As mentioned, in some cases, the cluster data store 264 includes one ormore indexer identifiers (also referred to herein as search peeridentifiers) corresponding to the indexers 206. In some cases, thecluster data store 264 can include a different indexer identifier foreach indexer 206. In some cases, if an indexer 206 becomes unresponsiveor unavailable, the cluster master 262 can update the cluster data store264 to remove an indexer identifier associated with that indexer 206. Inthis way, the cluster data store 264 can include up-to-date informationrelating to which indexers 206 are included and/or available. In certainembodiments, such as where an indexer identifier is associated withmultiple bucket map identifiers, the cluster master 262 can removereference to the indexer identifier for each of the bucket mapidentifiers. In some such cases, the cluster master 262 can replace theremoved indexer identifier with another indexer identifier correspondingto an indexer 206 that is assigned to process and/or search the datathat had previously been assigned to the now-unavailable indexer 206.

In some cases, the cluster data store 264 includes one or more metricsassociated with one or more of the indexers 206. For example, themetrics can include, but are not limited to, one or more performancemetrics such as CPU usage, memory use, error rate, network bandwidth,network throughput, time taken to process the data, time taken toschedule and execute a job or pipeline, the number of events, slices,and buckets that it is currently processing, time to download a message,time to decode a message, time to purge a message or send anacknowledgement, and/or time to renew messages if used or needed, or thelike. For example, the cluster data store 264 can include informationrelating to a utilization rate of an indexer 206, such as an indicationof which indexers 206, if any, are working at maximum capacity or at autilization rate that satisfies utilization threshold, such that theindexer 206 should not be used to process additional data for a time. Asanother example, the cluster data store 264 can include informationrelating to an availability or responsiveness of an indexer 206, anamount of processing resources in use by an indexer 206, or an amount ofmemory used by an indexer 206. Similarly, any one or any combination ofthe metrics related to the ingestors 252 can be stored in the clusterdata store 265.

In some cases, the cluster data store 264 includes one or more statusidentifiers associated with one or more of the indexers 206. Forexample, in some cases, a status identifier associated with one or moreof the indexers 206 can include information relating to an availabilityof an indexer 206. For example, the cluster data store 264 can includean indication of whether an indexer 206 is available or unavailable. Insome cases, as described herein, if an indexer 206 becomes unavailable,the cluster master 262 and/or the cluster data store 264 candisassociate that indexer 206 from (and/or can associate an availableindexer 206 to) one, some, or all bucket map identifiers, dataidentifiers, or the like, and can associate an available indexer 206. Inthis way, any data, processing, or querying that is assigned to anindexer 206 that becomes unavailable can be re-assigned to an availableindexer 206.

In some cases, a determination of the availability of an indexer 206 canbe based on a status update (or absence of a status update) from theindexer 206. In some instances, an indexer 206 is considered availableif it is instantiated or running, provides periodic status updates tothe cluster master 262, and/or is responsive communications from thecluster master 262. In some cases, an indexer 206 is consideredavailable if one or more metrics associated with the indexer 206satisfies a metrics threshold. For example, an indexer 206 can beconsidered available if a utilization rate of the indexer 206 satisfiesa utilization rate threshold. As another example, an indexer 206 canconsidered available if an amount of memory used by or available to theindexer 206 satisfies a memory threshold (non-limiting example:available memory >10% of total memory, etc.). As another example, anindexer 206 can be considered available if an amount of availableprocessing resources of the indexer 206 satisfies a processing resourcesthreshold (non-limiting example: CPU usage <90% of capacity, etc.).Similarly, in some cases, an indexer 206 can be considered unavailableif one or more, or some or all, metrics associated with the indexer 206do not satisfy a metrics threshold.

The cluster data store 264 can store information relating to data of thedata intake and query system 108. For example, the cluster data store264 can include information regarding where data is stored (for example,location information) in shared storage system 260, information usableto identify data (for example, data identifiers), etc. As describedherein, the cluster data store 264 can also include filter criteria, anidentification of which data satisfies the different filter criteria,the storage location of that data, and which indexers 206 are assignedto search that data, etc.

In some cases, the cluster data store 264 includes one or more dataidentifiers regarding data of the data intake and query system 108. Adata identifier can be used to uniquely identify a group of data in thedata intake and query system. For example if the group of datacorresponds to a bucket, then the data identifier can be a bucketidentifier. If the group of data corresponds to a slice of data, thenthe data identifier can correspond to a slice identifier, etc.

TABLE 3 Data Location in Shared Search Identifier Information StorageSystem Peer B56 Index: main Address1 A423 Time Range: ET: 2020-01-0100:12:15 LT: 2020-01-01 00:15:10 Slice5 Index: test Address560 B603 TimeRange: ET: 2020-01-01 05:20:00 LT: 2020-01-01 05:20:45

Table 3 illustrates an example data structure for a data identifierentry in the cluster data store 264. In the illustrated embodiment, thedata identifier “B56” corresponds to a bucket in the index “main” with atime range from “2020-01-01 00:12:15” to “2020-01-01 00:15:10.” A copyof the bucket “B56” is stored at location “Address1” in the sharedstorage system 260, and the search peer “A423” is responsible forsearching the bucket. The data identifier “Slice5” corresponds to aslice in the index “test” with a time range from “2020-01-01 05:20:00”to “2020-01-01 05:20:45.” Note that the time range of the slice issmaller than a bucket as buckets are typically made up of multipleslices of data. A copy of the slice “Slice5” is stored at location“Address560” in the shared storage system 260, and the search peer“B603” is responsible for searching the slice and processing the sliceto form a warm bucket.

Any one or any combination of the data structures shown in Tables 1, 2,and 3 can be used to organize, structure, or search, the data in thecluster data store 264. For example, in some cases, the data structuresof Table 1 can be used to identify a bucket map identifier and indexeridentifiers for a search head and/or identify data identifiers forspecific search peers. Similarly, the data structure of Table 2 can beused to identify data identifiers for specific search peers. The datastructure of Table 3 can be used to generate the data structures shownin Tables and 2, etc.

In some cases, the cluster data store 264 includes location informationregarding data in the data intake and query system 108. For example, thecluster data store 264 can include location information for some or allof the sets of one or more slices of data (before or after processing),some or all of the buckets, etc. Location information can include areference to a location at which a group of data is stored. The locationinformation can identify a location in local storage (for example,identifying a particular indexer 206 and/or data store 208) and/or alocation in the shared storage system 260.

The cluster master 262 can identify indexers 206 and can maintainindexer identifiers in the cluster data store 264. In addition, thecluster data store 264 can store an indexer assignment listing thatassociates indexers 206 with data identifiers associated with the groupsof data that the indexers 206 is assigned to process/index, store, orsearch. A non-limiting embodiment of a data structure for storingindexer assignment listings is shown above with reference to Table 2.The cluster master 262 can maintain indexer assignments that identifythe indexers 206 that are assigned to process the groups of data. Insome cases, the cluster master 262 can determine an indexer assignmentbased on information received from the indexer 206. For example, thecluster master 262 can create or update an indexer assignment inresponse to receiving a data identifier from the indexer 206. Thecluster master 262 can use the indexer assignments to determine whichindexer 206 is assigned to process, store, or search a particular groupof data. As a non-limiting example, the indexer assignments can identifythat a first indexer is assigned to process a first set of one or moreslices of data. However, in response to a determination that the firstindexer is unavailable, failing, or otherwise did not process the firstset of one or more slices of data, the cluster master 262 can un-assignthe first indexer from the first set of one or more slices of dataand/or assign a second indexer to process the first set of one or moreslices of data, and can update the indexer assignments.

If an indexer 206 later deletes data from its local storage, it cancommunicate this change to the cluster master 262. The cluster master262 can update the indexer assignment to indicate that the indexer 206no longer has the data stored locally. In some such cases, the clustermaster 262 can assign an indexer 206 to be responsible for searching thedata. For example, the cluster master 262 can assign the same indexer206 that had the data originally, other indexers 206 that are processingdata, or indexers 206 that do not process or store, data but arededicated to searching data. The cluster master 262 can store theupdated assignment in the cluster data store 264.

In a similar fashion, the cluster master 262 and/or cluster data store264 can store any one or any combination of the aforementioned pieces ofinformation with regard to the ingestors 252. For example, the clustermaster 262 and/or cluster data store 264 can store ingestor identifiers,metrics, status identifiers, etc. Further, the cluster master 262 canmake any type of determination about the availability, capacity, and/orutilization of the ingestors 252. Further, as described herein, aseparate component or monitoring component can be used to implement anyone or any combination of the aforementioned features of the clustermaster 262.

2.6. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIGS. 2A and 2B comprises several system components, including one ormore forwarders, indexers, and search heads. In some environments, auser of a data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIGS. 2A and 2B, thenetworked computer system 300 includes input data sources 202 andforwarders 204. These input data sources and forwarders may be in asubscriber's private computing environment. Alternatively, they might bedirectly managed by the service provider as part of the cloud service.In the example system 300, one or more forwarders 204 and client devices302 are coupled to a cloud-based data intake and query system 306 viaone or more networks 304. Network 304 broadly represents one or moreLANs, WANs, cellular networks, intranetworks, internetworks, etc., usingany of wired, wireless, terrestrial microwave, satellite links, etc.,and may include the public Internet, and is used by client devices 302and forwarders 204 to access the system 306. Similar to the system of38, each of the forwarders 204 may be configured to receive data from aninput source and to forward the data to other components of the system306 for further processing.

In some embodiments, a cloud-based data intake and query system 306 maycomprise a plurality of system instances 308. In general, each systeminstance 308 may include one or more computing resources managed by aprovider of the cloud-based system 306 made available to a particularsubscriber. The computing resources comprising a system instance 308may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 302 to access a web portal or otherinterface that enables the subscriber to configure an instance 308.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers, andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 308) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment, such as SPLUNK® ENTERPRISE, and acloud-based environment, such as SPLUNK CLOUD™, are centrally visible).

2.7. Searching Externally-Archived Data

FIG. 4 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the Splunk® Analytics for Hadoop® system provided bySplunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop®represents an analytics platform that enables business and IT teams torapidly explore, analyze, and visualize data in Hadoop® and NoSQL datastores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 404 over network connections420. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 4 illustrates thatmultiple client devices 404 a, 404 b . . . 404 n may communicate withthe data intake and query system 108. The client devices 404 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 4 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a software developerkit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 410. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively, or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data intake and query system108 through a network interface 420, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

2.7.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which can then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and can immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically, the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the machine data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process can be configured to operate inthe reporting mode only. Also, the ERP process can be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all machine data that is responsive to the searchquery request before the ERP process starts returning results; rather,the reporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.8. Data Ingestion

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments. The data flow illustrated in FIG.5A is provided for illustrative purposes only; those skilled in the artwould understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

2.8.1. Input

At block 502, a forwarder receives data from an input source, such as adata source 202 shown in FIGS. 2A and 2B. A forwarder initially mayreceive the data as a raw data stream generated by the input source. Forexample, a forwarder may receive a data stream from a log file generatedby an application server, from a stream of network data from a networkdevice, or from any other source of data. In some embodiments, aforwarder receives the raw data and may segment the data stream into“blocks”, possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someembodiments, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The data intake and query system allows forwarding of data from one dataintake and query instance to another, or even to a third-party system.The data intake and query system can employ different types offorwarders in a configuration.

In some embodiments, a forwarder may contain the essential componentsneeded to forward data. A forwarder can gather data from a variety ofinputs and forward the data to an indexer for indexing and searching. Aforwarder can also tag metadata (e.g., source, source type, host, etc.).

In some embodiments, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder can parse data before forwarding the data (e.g., can associatea time stamp with a portion of data and create an event, etc.) and canroute data based on criteria such as source or type of event. Theforwarder can also index data locally while forwarding the data toanother indexer.

2.8.2. Parsing

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In some embodiments,to organize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries withinthe received data that indicate the portions of machine data for events.In general, these properties may include regular expression-based rulesor delimiter rules where, for example, event boundaries may be indicatedby predefined characters or character strings. These predefinedcharacters may include punctuation marks or other special charactersincluding, for example, carriage returns, tabs, spaces, line breaks,etc. If a source type for the data is unknown to the indexer, an indexermay infer a source type for the data by examining the structure of thedata. Then, the indexer can apply an inferred source type definition tothe data to create the events.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5B (and FIG. 5C) is block diagram illustrating embodiments ofvarious data structures for storing data processed by the system 108,such as data processed by an indexer 206. FIG. 5B includes an expandedview illustrating an example of machine data stored in a data store 550of the data storage system 116. It will be understood that the depictionof machine data and associated metadata as rows and columns in the table559 of FIG. 5B is merely illustrative and is not intended to limit thedata format in which the machine data and metadata is stored in variousembodiments described herein. In one particular embodiment, machine datacan be stored in a compressed or encrypted format. In such embodiments,the machine data can be stored with or be associated with data thatdescribes the compression or encryption scheme with which the machinedata is stored. The information about the compression or encryptionscheme can be used to decompress or decrypt the machine data, and anymetadata with which it is stored, at search time.

In the illustrated embodiment of FIG. 5B the data store 550 includes adirectory 552 (individually referred to as 552A, 552B) for each index(or partition) that contains a portion of data stored in the data store550 and a sub-directory 554 (individually referred to as 554A, 554B,554C) for one or more buckets of the index. In the illustratedembodiment of FIG. 5B, each sub-directory 554 corresponds to a bucketand includes an event data file 556 (individually referred to as 556A,556B, 556C) and an inverted index 558 (individually referred to as 558A,558B, 558C). However, it will be understood that each bucket can beassociated with fewer or more files and each sub-directory 554 can storefewer or more files.

In the illustrated embodiment, the data store 550 includes a_maindirectory 552A associated with an index “_main” and a_test directory552B associated with an index “_test.” However, the data store 550 caninclude fewer or more directories. In some embodiments, multiple indexescan share a single directory or all indexes can share a commondirectory. Additionally, although illustrated as a single data store550, it will be understood that the data store 550 can be implemented asmultiple data stores storing different portions of the information shownin FIG. 5B. For example, a single index can span multiple directories ormultiple data stores.

Furthermore, although not illustrated in FIG. 5B, it will be understoodthat, in some embodiments, the data store 550 can include directoriesfor each tenant and sub-directories for each index of each tenant, orvice versa. Accordingly, the directories 552A and 552B can, in certainembodiments, correspond to sub-directories of a tenant or includesub-directories for different tenants.

In the illustrated embodiment of FIG. 5B, two sub-directories 554A, 554Bof the _main directory 552A and one sub-directory 552C of the _testdirectory 552B are shown. The sub-directories 554A, 554B, 554C cancorrespond to buckets of the indexes associated with the directories552A, 552B. For example, the sub-directories 554A and 554B cancorrespond to buckets “B1” and “B2,” respectively, of the index “_main”and the sub-directory 554C can correspond to bucket “B1” of the index“_test.” Accordingly, even though there are two “B1” buckets shown, aseach “B1” bucket is associated with a different index (and correspondingdirectory 552), the system 108 can uniquely identify them.

Although illustrated as buckets “B1” and “B2,” it will be understoodthat the buckets (and/or corresponding sub-directories 554) can be namedin a variety of ways. In certain embodiments, the bucket (orsub-directory) names can include information about the bucket. Forexample, the bucket name can include the name of the index with whichthe bucket is associated, a time range of the bucket, etc.

As described herein, each bucket can have one or more files associatedwith it, including, but not limited to one or more raw machine datafiles, bucket summary files, filter files, inverted indexes (alsoreferred to herein as high performance indexes or keyword indexes),permissions files, configuration files, etc. In the illustratedembodiment of FIG. 5B, the files associated with a particular bucket canbe stored in the sub-directory corresponding to the particular bucket.Accordingly, the files stored in the sub-directory 554A can correspondto or be associated with bucket “B1,” of index “_main,” the files storedin the sub-directory 554B can correspond to or be associated with bucket“B2” of index “_main,” and the files stored in the sub-directory 554Ccan correspond to or be associated with bucket “B1” of index “_test.”

FIG. 5B further illustrates an expanded event data file 556C showing anexample of data that can be stored therein. In the illustratedembodiment, four events 560, 562, 564, 566 of the machine data file 556Care shown in four rows. Each event 560-566 includes machine data 570 anda timestamp 572. The machine data 570 can correspond to machine datareceived and processed by the system 108, such as machine data receivedand processed by the indexer 206.

Metadata 574-578 associated with the events 560-566 is also shown in thetable 559. In the illustrated embodiment, the metadata 574-578 includesinformation about a host 574, source 576, and sourcetype 578 associatedwith the events 560-566. Any of the metadata can be extracted from thecorresponding machine data, or supplied or defined by an entity, such asa user or computer system. The metadata fields 574-578 can become partof, stored with, or otherwise associated with the events 560-566. Incertain embodiments, the metadata 574-578 can be stored in a separatefile of the sub-directory 554C and associated with the machine data file556C. In some cases, while the timestamp 572 can be extracted from theraw data of each event, the values for the other metadata fields may bedetermined by the system 108 (e.g., the indexers 206) based oninformation it receives pertaining to the host device 106 or data source202 of the data separate from the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, the machine data within anevent can be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. For example, the machine data of events560-566 can be identical to portions of the machine data used togenerate a particular event. Similarly, the entirety of machine datareceived by the system 108 (or an indexer 206) may be found acrossmultiple events. As such, unless certain information needs to be removedfor some reasons (e.g. extraneous information, confidentialinformation), all the raw machine data contained in an event can bepreserved and saved in its original form. Accordingly, the data store inwhich the event records are stored is sometimes referred to as a “rawrecord data store.” The raw record data store contains a record of theraw event data tagged with the various fields.

In other embodiments, the portion of machine data in an event can beprocessed or otherwise altered relative to the machine data used tocreate the event. For example, the machine data of a corresponding event(or events) may be modified such that only a portion of the machine datais stored as one or more events, or the machine data may be altered toremove duplicate data, confidential information, etc., before beingstored as one or more events.

In FIG. 5B, the first three rows of the table 559 present events 560,562, and 564 and are related to a server access log that recordsrequests from multiple clients processed by a server, as indicated byentry of “access.log” in the source column 576. In the example shown inFIG. 5B, each of the events 560-564 is associated with a discreterequest made to the server by a client. The raw machine data generatedby the server and extracted from a server access log can include the IPaddress 540 of the client, the user id 541 of the person requesting thedocument, the time 542 the server finished processing the request, therequest line 543 from the client, the status code 544 returned by theserver to the client, the size of the object 545 returned to the client(in this case, the gif file requested by the client) and the time spent546 to serve the request in microseconds. In the illustrated embodimentof FIG. 5B, the raw machine data retrieved from a server access log isretained and stored as part of the corresponding events 560-564 in thefile 556C.

Event 566 is associated with an entry in a server error log, asindicated by “error.log” in the source column 576 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 566 can be preserved and storedas part of the event 566.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5B is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

2.8.3. Indexing

At blocks 514 and 516, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair caninclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs canbe quickly located. In some embodiments, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some embodiments, the stored events are organizedinto “buckets,” where each bucket stores events associated with aspecific time range based on the timestamps associated with each event.This improves time-based searching, as well as allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk. In some embodiments, eachbucket may be associated with an identifier, a time range, and a sizeconstraint.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query. Insome embodiments, each bucket may be associated with an identifier, atime range, and a size constraint. In certain embodiments, a bucket cancorrespond to a file system directory and the machine data, or events,of a bucket can be stored in one or more files of the file systemdirectory. The file system directory can include additional files, suchas one or more inverted indexes, high performance indexes, permissionsfiles, configuration files, etc. A non-limiting example of a bucket isdescribed herein at least with reference to FIGS. 5B and 5C.

In some embodiments, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that can no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that can no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some embodiments, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

FIG. 5C illustrates an embodiment of another file that can be includedin one or more subdirectories 554 or buckets (described in greaterdetail herein at least with reference to FIG. 5B). Specifically, FIG. 5Cillustrates an exploded view of an embodiments of an inverted index 558Bin the sub-directory 554B, associated with bucket “B2” of the index“_main,” as well as an event reference array 580 associated with theinverted index 558B.

In some embodiments, the inverted indexes 558 can correspond to distincttime-series buckets. As such, each inverted index 558 can correspond toa particular range of time for an index. In the illustrated embodimentof FIG. 5C, the inverted indexes 558A, 558B correspond to the buckets“B1” and “B2,” respectively, of the index “_main,” and the invertedindex 558C corresponds to the bucket “B1” of the index “_test.” In someembodiments, an inverted index 558 can correspond to multipletime-series buckets (e.g., include information related to multiplebuckets) or inverted indexes 558 can correspond to a single time-seriesbucket.

Each inverted index 558 can include one or more entries, such as keyword(or token) entries 582 or field-value pair entries 584. Furthermore, incertain embodiments, the inverted indexes 558 can include additionalinformation, such as a time range 586 associated with the inverted indexor an index identifier 588 identifying the index associated with theinverted index 558. It will be understood that each inverted index 558can include less or more information than depicted. For example, in somecases, the inverted indexes 558 may omit a time range 586 and/or indexidentifier 588. In some such embodiments, the index associated with theinverted index 558 can be determined based on the location (e.g.,directory 552) of the inverted index 558 and/or the time range of theinverted index 558 can be determined based on the name of thesub-directory 554.

Token entries, such as token entries 582 illustrated in inverted index558B, can include a token 582A (e.g., “error,” “itemID,” etc.) and eventreferences 582B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 5C, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents located in the bucket “B2” of the index “_main.”

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, thesystem 108 (e.g., the indexers 206) can identify each word or string inan event as a distinct token and generate a token entry for theidentified word or string. In some cases, the system 108 (e.g., theindexers 206) can identify the beginning and ending of tokens based onpunctuation, spaces, etc. In certain cases, the system 108 (e.g., theindexers 206) can rely on user input or a configuration file to identifytokens for token entries 582, etc. It will be understood that anycombination of token entries can be included as a default, automaticallydetermined, or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries584 shown in inverted index 558B, can include a field-value pair 584Aand event references 584B indicative of events that include a fieldvalue that corresponds to the field-value pair (or the field-valuepair). For example, for a field-value pair sourcetype::sendmail, afield-value pair entry 584 can include the field-value pair“sourcetype::sendmail” and a unique identifier, or event reference, foreach event stored in the corresponding time-series bucket that includesa sourcetype “sendmail.”

In some cases, the field-value pair entries 584 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields“host,” “source,” and “sourcetype” can be included in the invertedindexes 558 as a default. As such, all of the inverted indexes 558 caninclude field-value pair entries for the fields “host,” “source,” and“sourcetype.” As yet another non-limiting example, the field-value pairentries for the field “IP_address” can be user specified and may onlyappear in the inverted index 558B or the inverted indexes 558A, 558B ofthe index “_main” based on user-specified criteria. As anothernon-limiting example, as the indexers 206 indexes the events, it canautomatically identify field-value pairs and create field-value pairentries 584. For example, based on the indexers' 206 review of events,it can identify IP_address as a field in each event and add theIP_address field-value pair entries to the inverted index 558B (e.g.,based on punctuation, like two keywords separated by an ‘=’ or ‘:’etc.). It will be understood that any combination of field-value pairentries can be included as a default, automatically determined, orincluded based on user-specified criteria.

With reference to the event reference array 580, each unique identifier590, or event reference, can correspond to a unique event located in thetime series bucket or machine data file 556B. The same event referencecan be located in multiple entries of an inverted index 558. For exampleif an event has a sourcetype “splunkd,” host “www1” and token “warning,”then the unique identifier for the event can appear in the field-valuepair entries 584 “sourcetype::splunkd” and “host::www1,” as well as thetoken entry “warning.” With reference to the illustrated embodiment ofFIG. 5C and the event that corresponds to the event reference 3, theevent reference 3 is found in the field-value pair entries 584“host::hostA,” “source::sourceB,” “sourcetype::sourcetypeA,” and“IP_address::91.205.189.15” indicating that the event corresponding tothe event references is from hostA, sourceB, of sourcetypeA, andincludes “91.205.189.15” in the event data.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex 558 may include four sourcetype field-value pair entries 584corresponding to four different sourcetypes of the events stored in abucket (e.g., sourcetypes: sendmail, splunkd, web_access, andweb_service). Within those four sourcetype field-value pair entries, anidentifier for a particular event may appear in only one of thefield-value pair entries. With continued reference to the exampleillustrated embodiment of FIG. 5C, since the event reference 7 appearsin the field-value pair entry “sourcetype::sourcetypeA,” then it doesnot appear in the other field-value pair entries for the sourcetypefield, including “sourcetype::sourcetypeB,” “sourcetype::sourcetypeC,”and “sourcetype::sourcetypeD.”

The event references 590 can be used to locate the events in thecorresponding bucket or machine data file 556. For example, the invertedindex 558B can include, or be associated with, an event reference array580. The event reference array 580 can include an array entry 590 foreach event reference in the inverted index 558B. Each array entry 590can include location information 592 of the event corresponding to theunique identifier (non-limiting example: seek address of the event,physical address, slice ID, etc.), a timestamp 594 associated with theevent, or additional information regarding the event associated with theevent reference, etc.

For each token entry 582 or field-value pair entry 584, the eventreference 582B, 584B, respectively, or unique identifiers can be listedin chronological order or the value of the event reference can beassigned based on chronological data, such as a timestamp associatedwith the event referenced by the event reference. For example, the eventreference 1 in the illustrated embodiment of FIG. 5C can correspond tothe first-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order (e.g.,based on time received or added to the machine data file), etc. Further,the entries can be sorted. For example, the entries can be sortedalphabetically (collectively or within a particular group), by entryorigin (e.g., default, automatically generated, user-specified, etc.),by entry type (e.g., field-value pair entry, token entry, etc.), orchronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 5C, the entries are sorted first by entrytype and then alphabetically.

In some cases, inverted indexes 558 can decrease the search time of aquery. For example, for a statistical query, by using the invertedindex, the system 108 (or the indexers 206 or search head 210) can avoidthe computational overhead of parsing individual events in a machinedata file 556. Instead, the system 108 can use the inverted index 558separate from the raw record data store to generate responses to thereceived queries.

2.9. Query Processing

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments. At block 602, a search head receives a search queryfrom a client. At block 604, the search head analyzes the search queryto determine what portion(s) of the query can be delegated to indexersand what portions of the query can be executed locally by the searchhead. At block 606, the search head distributes the determined portionsof the query to the appropriate indexers. In some embodiments, a searchhead cluster may take the place of an independent search head where eachsearch head in the search head cluster coordinates with peer searchheads in the search head cluster to schedule jobs, replicate searchresults, update configurations, fulfill search requests, etc. In someembodiments, the search head (or each search head) communicates with amaster node (also known as a cluster master, shown in FIG. 2B, but notshown in FIG. 2A) that provides the search head with a list of indexersto which the search head can distribute the determined portions of thequery. The master node maintains a list of active indexers and can alsodesignate which indexers may have responsibility for responding toqueries over certain sets of events. A search head may communicate withthe master node before the search head distributes queries to indexersto discover the addresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some embodiments, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

2.10. Pipelined Search Language

Various embodiments of the present disclosure can be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can operate to search or filterfor specific data in particular set of data. The results of the firstcommand can then be passed to another command listed later in thecommand sequence for further processing.

In various embodiments, a query can be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querycan be formulated as a sequence of SPL commands Some or all of the SPLcommands in the sequence of SPL commands can be separated from oneanother by a pipe symbol “I”. In such embodiments, a set of data, suchas a set of events, can be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “I”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“I” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein can be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms can include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results can then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence can include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some embodiments, the summary can include a graph, chart,metric, or other visualization of the data. An aggregation function caninclude analysis or calculations to return an aggregate value, such asan average value, a sum, a maximum value, a root mean square,statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it can perform “filtering”as well as “processing” functions. In other words, a single query caninclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command can filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneembodiment, the set of result data can be in the form of a dynamicallycreated table. Each command in a particular query can redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query can be considered a row with a column for eachfield value. Columns contain basic information about the data and alsomay contain data that has been dynamically extracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed embodiments. The query 630 can be inputted by the user into asearch. The query comprises a search, the results of which are piped totwo commands (namely, command 1 and command 2) that follow the searchstep.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query can comprise search terms “sourcetype=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms“sourcetype=syslog ERROR” from search command 630. By way of furtherexample, instead of a search step, the set of events at the head of thepipeline may be generating by a call to a pre-existing inverted index(as will be explained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which summarizes the events into a list of the top10 users and displays the user, count, and percentage.

Finally, at block 644, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields-percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query.

2.11. Field Extraction

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) can be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “I” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), can beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules cancomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself canspecify one or more extraction rules.

The search head 210 can apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 701 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 702. The userthen sends a message to the customer support server 703 to complainabout the order failing to complete. The three systems 701, 702, and 703are disparate systems that do not have a common logging format. Theorder application 701 sends log data 704 to the data intake and querysystem in one format, the middleware code 702 sends error log data 705in a second format, and the support server 703 sends log data 706 in athird format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it can correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface can display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments.

If a user inputs a search query into search bar 710 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data intake and query systemsearches for those keywords directly in the event data 711 stored in theraw record data store. Note that while FIG. 7B only illustrates fourevents 712, 713, 714, 715, the raw record data store (corresponding todata store 208 in FIGS. 2A and 2B) may contain records for millions ofevents.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, the events 712, 713, and 714, will be identifiedbased on the results returned from the keyword index. As noted above,the index contains reference pointers to the events containing thekeyword, which allows for efficient retrieval of the relevant eventsfrom the raw record data store.

If a user searches for a keyword that has not been indexed by theindexer, the data intake and query system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 7B. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the data intake and query system will search theevent data directly and return the first event 712. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata of the events 712-715 is accessed from the raw data record store toservice the keyword search. In the case where the keyword has beenindexed, the index will contain a reference pointer that will allow fora more efficient retrieval of the event data from the data store. If thekeyword has not been indexed, the search engine will need to searchthrough all the records in the data store to service the search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g, emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov15 09:33:22 johnmedlock.”

The data intake and query system advantageously allows for search timefield extraction. In other words, fields can be extracted from the eventdata at search time using late-binding schema as opposed to at dataingestion time, which was a major limitation of the prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a search query, the dataintake and query system determines if the query references a “field.”For example, a query may request a list of events where the “clientip”field equals “127.0.0.1.” If the query itself does not specify anextraction rule and if the field is not a metadata field, e.g., time,host, source, source type, etc., then in order to determine anextraction rule, the search engine may, in one or more embodiments, needto locate configuration file 716 during the execution of the search asshown in FIG. 7B.

Configuration file 716 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules can compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 716.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 716. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 716 to event data that it receives from indexers 206.Indexers 206 may apply the extraction rules from the configuration fileto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 716will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 715 also contains“clientip” field, however, the “clientip” field is in a different formatfrom the events 712, 713, and 714. To address the discrepancies in theformat and content of the different types of events, the configurationfile will also need to specify the set of events that an extraction ruleapplies to, e.g., extraction rule 717 specifies a rule for filtering bythe type of event and contains a regular expression for parsing out thefield value. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 716 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcecan have the same format.

The field extraction rules stored in configuration file 716 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 716 to retrieve extraction rule 717 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine can then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, the events 712,713, and 714 would be returned in response to the user query. In thismanner, the search engine can service queries containing field criteriain addition to queries containing keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the data intakeand query system maintains the underlying raw data and uses late-bindingschema for searching the raw data, it enables a user to continueinvestigating and learn valuable insights about the raw data long afterdata ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data intake and query system to search andcorrelate data across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 716 allows the record data store tobe field searchable. In other words, the raw record data store can besearched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand can be defined in configuration file 716 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and can search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query search engine to count the number of events wherethe “clientip” field equals “127.0.0.1.”

2.12. Example Search Screen

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example embodiments. Search screen800 includes a search bar 802 that accepts user input in the form of asearch string. It also includes a time range picker 812 that enables theuser to specify a time range for the search. For historical searches(e.g., searches based on a particular historical time range), the usercan select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user can select the size of a preceding time window tosearch for real-time events. Search screen 800 also initially displays a“data summary” dialog as is illustrated in FIG. 8B that enables the userto select different sources for the events, such as by selectingspecific hosts and log files.

After the search is executed, the search screen 800 in FIG. 8A candisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also displays anevents list 808 that enables a user to view the machine data in each ofthe returned events.

The events tab additionally displays a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, sourcetype) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usercan select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

2.13. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user can simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject can be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user can retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models can include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects canbe designed by knowledge managers in an organization, and they canenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent subset.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some embodiments, the data intake and query system 108 provides theuser with the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also can be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects can be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata.

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example embodiments. The reportgeneration process may be driven by a predefined data model object, suchas a data model object defined and/or saved via a reporting applicationor a data model object obtained from another source. A user can load asaved data model object using a report editor. For example, the initialsearch query and fields used to drive the report editor may be obtainedfrom a data model object. The data model object that is used to drive areport generation process may define a search and a set of fields. Uponloading of the data model object, the report generation process mayenable a user to use the fields (e.g., the fields defined by the datamodel object) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 9 illustrates an example interactive data modelselection graphical user interface 900 of a report editor that displaysa listing of available data models 901. The user may select one of thedata models 902.

FIG. 10 illustrates an example data model object selection graphicaluser interface 1000 that displays available data objects 1001 for theselected data object model 902. The user may select one of the displayeddata model objects 1002 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 1100 shown in FIG. 11A may display an interactive listing ofautomatic field identification options 1101 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 1102, the “SelectedFields” option 1103, or the “Coverage” option (e.g., fields with atleast a specified % of coverage) 1104). If the user selects the “AllFields” option 1102, all of the fields identified from the events thatwere returned in response to an initial search query may be selected.That is, for example, all of the fields of the identified data modelobject fields may be selected. If the user selects the “Selected Fields”option 1103, only the fields from the fields of the identified datamodel object fields that are selected by the user may be used. If theuser selects the “Coverage” option 1104, only the fields of theidentified data model object fields meeting a specified coveragecriteria may be selected. A percent coverage may refer to the percentageof events returned by the initial search query that a given fieldappears in. Thus, for example, if an object dataset includes 10,000events returned in response to an initial search query, and the“avg_age” field appears in 854 of those 10,000 events, then the“avg_age” field would have a coverage of 8.54% for that object dataset.If, for example, the user selects the “Coverage” option and specifies acoverage value of 2%, only fields having a coverage value equal to orgreater than 2% may be selected. The number of fields corresponding toeach selectable option may be displayed in association with each option.For example, “97” displayed next to the “All Fields” option 1102indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 1103indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 1104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 11B illustrates an example graphical user interface screen 1105displaying the reporting application's “Report Editor” page. The screenmay display interactive elements for defining various elements of areport. For example, the page includes a “Filters” element 1106, a“Split Rows” element 1107, a “Split Columns” element 1108, and a “ColumnValues” element 1109. The page may include a list of search results1111. In this example, the Split Rows element 1107 is expanded,revealing a listing of fields 1110 that can be used to define additionalcriteria (e.g., reporting criteria). The listing of fields 1110 maycorrespond to the selected fields. That is, the listing of fields 1110may list only the fields previously selected, either automaticallyand/or manually by a user. FIG. 11C illustrates a formatting dialogue1112 that may be displayed upon selecting a field from the listing offields 1110. The dialogue can be used to format the display of theresults of the selection (e.g., label the column for the selected fieldto be displayed as “component”).

FIG. 11D illustrates an example graphical user interface screen 1105including a table of results 1113 based on the selected criteriaincluding splitting the rows by the “component” field. A column 1114having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row for aparticular field, such as the value “BucketMover” for the field“component”) occurs in the set of events responsive to the initialsearch query.

FIG. 12 illustrates an example graphical user interface screen 1200 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1201 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1203. A count of the number of successful purchases foreach product is displayed in column 1204. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1205, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 13 illustrates an example graphical user interface 1300 thatdisplays a set of components and associated statistics 1301. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.), wherethe format of the graph may be selected using the user interfacecontrols 1302 along the left panel of the user interface 1300. FIG. 14illustrates an example of a bar chart visualization 1400 of an aspect ofthe statistical data 1301. FIG. 15 illustrates a scatter plotvisualization 1500 of an aspect of the statistical data 1301.

2.14. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the data intake and query system also employs anumber of unique acceleration techniques that have been developed tospeed up analysis operations performed at search time. These techniquesinclude: (1) performing search operations in parallel across multipleindexers; (2) using a keyword index; (3) using a high performanceanalytics store; and (4) accelerating the process of generating reports.These novel techniques are described in more detail below.

2.14.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 16 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleembodiments. FIG. 16 illustrates how a search query 1602 received from aclient at a search head 210 can split into two phases, including: (1)subtasks 1604 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 1606 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 1602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 1602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 1604, and then distributes searchquery 1604 to distributed indexers, which are also referred to as“search peers” or “peer indexers.” Note that search queries maygenerally specify search criteria or operations to be performed onevents that meet the search criteria. Search queries may also specifyfield names, as well as search criteria for the values in the fields oroperations to be performed on the values in the fields. Moreover, thesearch head may distribute the full search query to the search peers asillustrated in FIG. 6A, or may alternatively distribute a modifiedversion (e.g., a more restricted version) of the search query to thesearch peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 1606 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

2.14.2. Keyword Index

As described above with reference to the flow charts in FIG. 5A and FIG.6A, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.14.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index can keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the search engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiments, a query cancomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 6B, a set of events can be generated at block 640by either using a “collection” query to create a new inverted index orby calling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 7C,an inverted index 722 can be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in inverted index 722 includes an event reference value thatreferences the location of a source record in the field searchable datastore. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.In FIG. 7C, prior to running the collection query that generates theinverted index 722, the field name “clientip” may need to be defined ina configuration file by specifying the “access_combined” source type anda regular expression rule to parse out the client IP address.Alternatively, the collection query may contain an explicit definitionfor the field name “clientip” which may obviate the need to referencethe configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 722) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 7C rather than viewing the fields within the invertedindex 722, a user may want to generate a count of all client requestsfrom IP address “127.0.0.1.” In this case, the search engine wouldsimply return a result of “4” rather than including details about theinverted index 722 in the information provided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem can be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may be generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety.

2.14.3.1 Extracting Event Data Using Posting

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system can use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system can use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneembodiment, include syntax that can direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 7C, if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user can generate a query that explicitlydirects or pipes the contents of the already generated inverted index722 to another filtering step requesting the user ids for the entries ininverted index 722 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “matt” would be returned to the user from the generatedresults table 725.

In one embodiment, the same methodology can be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the search engine would againuse the reference values stored in inverted index 722 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 731, 732, 733and 734. Once, the corresponding object sizes have been extracted (i.e.2326, 2900, 2920, and 5000), the average can be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that could expeditethis query. In one embodiment, prior to running any search comprising afield-value pair, for example, a search engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the search comprising the field-value pair.Accordingly, the search engine would automatically use the pre-generatedinverted index, e.g., index 722 to generate the results without anyuser-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data intake and query system includes one or more forwarders thatreceive raw machine data from a variety of input data sources, and oneor more indexers that process and store the data in one or more datastores. By distributing events among the indexers and data stores, theindexers can analyze events for a query in parallel. In one or moreembodiments, a multiple indexer implementation of the search systemwould maintain a separate and respective inverted index for each of theabove-described time-specific buckets that stores events for a specifictime range. A bucket-specific inverted index includes entries forspecific field-value combinations that occur in events in the specificbucket. As explained above, a search head would be able to correlate andsynthesize data from across the various buckets and indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query can be used to determine a set of event data thatcan be further limited by filtering or processing in accordance with thedisclosed embodiments.

At block 742, a query is received by a data intake and query system. Insome embodiments, the query can be received as a user generated queryentered into search bar of a graphical user search interface. The searchinterface also includes a time range control element that enablesspecification of a time range for the query.

At block 744, an inverted index is retrieved. Note, that the invertedindex can be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine can be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in most embodiments,the search engine will employ the inverted index separate from the rawrecord data store to generate responses to the received queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one embodiment, summarization information can beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query can be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

2.14.4. Accelerating Report Generation

In some embodiments, a data server system such as the data intake andquery system can accelerate the process of periodically generatingupdated reports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

2.15. Security Features

The data intake and query system provides various schemas, dashboards,and visualizations that simplify developers' tasks to createapplications with additional capabilities. One such application is anenterprise security application, such as SPLUNK® ENTERPRISE SECURITY,which performs monitoring and alerting operations and includes analyticsto facilitate identifying both known and unknown security threats basedon large volumes of data stored by the data intake and query system. Theenterprise security application provides the security practitioner withvisibility into security-relevant threats found in the enterpriseinfrastructure by capturing, monitoring, and reporting on data fromenterprise security devices, systems, and applications. Through the useof the data intake and query system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data intake and querysystem search-time normalization techniques, saved searches, andcorrelation searches to provide visibility into security-relevantthreats and activity and generate notable events for tracking. Theenterprise security application enables the security practitioner toinvestigate and explore the data to find new or unknown threats that donot follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which can be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events can be detected in a number of ways, such as: (1) a usercan notice a correlation in events and can manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user can define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application can indicate that the one or more events correspond to anotable event; and the like. A user can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The enterprise security application provides various visualizations toaid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 17A illustrates anexample key indicators view 1700 that comprises a dashboard, which candisplay a value 1701, for various security-related metrics, such asmalware infections 1702. It can also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 17B illustrates an example incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in time intervals over theselected time range. It additionally displays an events list 1714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

2.16. Data Center Monitoring

As mentioned above, the data intake and query platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is a virtual machine monitoringapplication, such as SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the virtual machine monitoring application stores largevolumes of minimally processed machine data, such as performanceinformation and log data, at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. Suchperformance metrics are described in U.S. patent application Ser. No.14/167,256, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

To facilitate retrieving information of interest from performance dataand log files, the virtual machine monitoring application providespre-specified schemas for extracting relevant values from differenttypes of performance-related events, and also enables a user to definesuch schemas.

The virtual machine monitoring application additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Example node-expansionoperations are illustrated in FIG. 17C, wherein nodes 1733 and 1734 areselectively expanded. Note that nodes 1731-1739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state or anunknown/offline state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVEMONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015,and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREEWITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which ishereby incorporated by reference in its entirety for all purposes.

The virtual machine monitoring application also provides a userinterface that enables a user to select a specific time range and thenview heterogeneous data comprising events, log data, and associatedperformance metrics for the selected time range. For example, the screenillustrated in FIG. 17D displays a listing of recent “tasks and events”and a listing of recent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus1742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,256, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

2.17. IT Service Monitoring

As previously mentioned, the data intake and query platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is an IT monitoring application, such as SPLUNK® ITSERVICE INTELLIGENCE™, which performs monitoring and alertingoperations. The IT monitoring application also includes analytics tohelp an analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the data intake and query system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related events. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, an IT monitoring application system stores large volumes ofminimally-processed service-related data at ingestion time for laterretrieval and analysis at search time, to perform regular monitoring, orto investigate a service issue. To facilitate this data retrievalprocess, the IT monitoring application enables a user to define an IToperations infrastructure from the perspective of the services itprovides. In this service-centric approach, a service such as corporatee-mail may be defined in terms of the entities employed to provide theservice, such as host machines and network devices. Each entity isdefined to include information for identifying all of the events thatpertains to the entity, whether produced by the entity itself or byanother machine, and considering the many various ways the entity may beidentified in machine data (such as by a URL, an IP address, or machinename). The service and entity definitions can organize events around aservice so that all of the events pertaining to that service can beeasily identified. This capability provides a foundation for theimplementation of Key Performance Indicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the IT monitoring application. Each KPI measures an aspect ofservice performance at a point in time or over a period of time (aspectKPI's). Each KPI is defined by a search query that derives a KPI valuefrom the machine data of events associated with the entities thatprovide the service. Information in the entity definitions may be usedto identify the appropriate events at the time a KPI is defined orwhenever a KPI value is being determined. The KPI values derived overtime may be stored to build a valuable repository of current andhistorical performance information for the service, and the repository,itself, may be subject to search query processing. Aggregate KPIs may bedefined to provide a measure of service performance calculated from aset of service aspect KPI values; this aggregate may even be takenacross defined timeframes and/or across multiple services. A particularservice may have an aggregate KPI derived from substantially all of theaspect KPI's of the service to indicate an overall health score for theservice.

The IT monitoring application facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, the IT monitoring application implements atranslation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’and ‘informational’. Thresholds associated with a particular KPIdefinition determine ranges of values for that KPI that correspond tothe various state values. In one case, KPI values 95-100 may be set tocorrespond to ‘critical’ in the state domain KPI values from disparateKPI's can be processed uniformly once they are translated into thecommon state values using the thresholds. For example, “normal 80% ofthe time” can be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value can be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. The IT monitoring application can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in the IT monitoring application can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in the IT monitoring applicationcan also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in the IT monitoring application can also be associated witha service by means of a service definition rule. Processing the ruleresults in the matching entity definitions being associated with theservice definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, the IT monitoring application can recognize notableevents that may indicate a service performance problem or othersituation of interest. These notable events can be recognized by a“correlation search” specifying trigger criteria for a notable event:every time KPI values satisfy the criteria, the application indicates anotable event. A severity level for the notable event may also bespecified. Furthermore, when trigger criteria are satisfied, thecorrelation search may additionally or alternatively cause a serviceticket to be created in an IT service management (ITSM) system, such asa systems available from ServiceNow, Inc., of Santa Clara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of events and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. The IT monitoringapplication provides a service monitoring interface suitable as the homepage for ongoing IT service monitoring. The interface is appropriate forsettings such as desktop use or for a wall-mounted display in a networkoperations center (NOC). The interface may prominently display aservices health section with tiles for the aggregate KPI's indicatingoverall health for defined services and a general KPI section with tilesfor KPI's related to individual service aspects. These tiles may displayKPI information in a variety of ways, such as by being colored andordered according to factors like the KPI state value. They also can beinteractive and navigate to visualizations of more detailed KPIinformation.

The IT monitoring application provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

The IT monitoring application provides a visualization showing detailedtime-series information for multiple KPI's in parallel graph lanes. Thelength of each lane can correspond to a uniform time range, while thewidth of each lane may be automatically adjusted to fit the displayedKPI data. Data within each lane may be displayed in a user selectablestyle, such as a line, area, or bar chart. During operation a user mayselect a position in the time range of the graph lanes to activate laneinspection at that point in time. Lane inspection may display anindicator for the selected time across the graph lanes and display theKPI value associated with that point in time for each of the graphlanes. The visualization may also provide navigation to an interface fordefining a correlation search, using information from the visualizationto pre-populate the definition.

The IT monitoring application provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

The IT monitoring application provides pre-specified schemas forextracting relevant values from the different types of service-relatedevents. It also enables a user to define such schemas.

3.0. PROCESSING DATA USING INGESTORS AND A MESSAGE BUS

As described herein, the data intake and query system 108 can useingestors 252 and a message bus 254 to process data.

3.1. Ingestor Data Flow Example

FIG. 18 is a data flow diagram illustrating an embodiment of the dataflow and communications between a variety of the components of adistributed data processing system, such as the data intake and querysystem 108 to generate and place events in a message bus 254. The dataflow diagram of FIG. 18 illustrates an example of data flow andcommunications between a data source 202, forwarder 204, ingestor 252,and a message bus 254. However, it will be understood, that in some ofembodiments, one or more of the functions described herein with respectto FIG. 18 can be omitted, performed concurrently or in a differentorder and/or performed by a different component of the data intake andquery system 108. Further, a similar process can occur between differentcomponents. For example, rather than a forwarder 204 obtaining andforwarding data to the ingestor 252, a HEC or other component may obtainand forward data to the ingestor 252. Accordingly, the illustratedembodiment and description should not be construed as limiting.

At (1), a forwarder 204 obtains data from a data source 202. Asdescribed herein, the obtained data can be raw machine data, metrics orother data. The data can be obtained from one or more log files or othersources on the data source 202, etc.

At (2), the forwarder 204 forwards the data to an ingestor 252. In somecases, the forwarder 204 can perform some processing on the data beforeforwarding it to the ingestor 252. For example, the forwarder can appendmetadata to the data, such as, a host or source to the data. In certaincases, the forwarder 204 can perform additional processing on the data,such as generating events from the data.

At (3) the ingestor 252 generates events and groups events. In caseswhere the forwarder 204 has generated events or partially processed thedata, the ingestor 252 can dynamically determine what processing is tobe done and process the data or events depending on what processing hasalready been done and where the forwarder 204 has not generated events,the ingestor 252 can generate the events. As described herein,generating events can include, parsing the received data, applying linebreaking to the data, merging lines to form multi-line events,determining host, source, and sourcetype of the data, applying regularexpression rules to the data, extracting information from the data, suchas punctuation, timestamps, etc. After generating an event, the ingestor252 can add the event to a buffer or queue. Additional processes of theingestor 252 can group events from the buffer or queue and prepare themfor communication to the message bus 254. As part of this, the ingestor252 can serialize or encode the group of events and determine the sizeof the group of events (or encoded group of events).

At (4), the ingestor 252 can send the group of events to the message bus254. Depending on the size of the group of events, the ingestor 252 cansend the group of events in different ways. If the size of the group ofevents satisfies or exceeds a message size threshold, the ingestor 252can store the group of events in a data store 258 of the message bus254, obtain a location reference to the storage location of the group ofevents in the data store 258, and communicate the location reference toa message queue 256 of the message bus 254. If the size of the eventsdoes not satisfy or is less than the message size threshold, theingestor 252 can send the (encoded) group of events to the message queue256 of the message bus 254.

At (5), the message bus 254 can process messages related to the groupsof events. As described herein, the message bus 254 can include amessage queue 256 and a data store 258. The message queue 256 can beimplemented as a pub-sub and can make messages available to subscribers.The messages in the message queue 256 can include groups of events(encoded or decoded) or location references to groups of events (encodedor decoded) that are stored in the data store 258. The message queue 256can track which messages have been sent to which indexers 206. Inaddition, the message queue 256 can track the messages as they areprovided to indexers 206. Once a particular message has beenacknowledged by an indexer 206 (e.g., after all of the events associatedwith the message have been stored in the shared storage system 260 aspart of a slice or bucket), the message queue 256 can delete theparticular message (and corresponding events). In cases where thegrouped events are stored in the data store 258 and the message queue256 includes a reference to the grouped events in the data store 258,the grouped events in the data store 258 can be deleted along with thecorresponding message in the message queue 256.

At (6), the message bus 254 can acknowledge that the group of eventshave been stored in a recoverable manner such that if message bus 254 orother component of the data and intake query system 108 fails, theevents can be recovered and will not be lost. In response, at (7), theingestor 252 can acknowledge that the group of events have been stored.Based on the acknowledgement, the forwarder 204 can delete the data thatcorresponds to the group of events and/or communicate with the datasource 202 to delete the data that corresponds to the group of events.

Fewer more or different functions can be performed by the differentcomponents of the data intake and query system 108. Further, it will beunderstood that the functions described herein can be performedconcurrently for different data, multiple events, and/or messages.Accordingly, in some embodiments, an ingestor 252 can concurrentlygenerate multiple events from different data, generate multiple groupsof events, store multiple groups of events to the data store 258,communicate multiple references associated with different groups ofevents stored in the data store 258 to the message queue 256, and/orcommunicate multiple groups of events to the message queue 256. It willfurther be understood that multiple ingestors 252 can concurrentlyperform these functions for different data received from differentsources.

3.2. Ingestor Flow Examples

FIG. 19 is a flow diagram illustrative of an embodiment of a routine1900, implemented by a computing device of a distributed data processingsystem, for communicating groups of events to a message bus 254.Although described as being implemented by the ingestor 252 of the dataintake and query system 108, it will be understood that the elementsoutlined for routine 1900 can be implemented by any one or a combinationof computing devices/components that are associated with the data intakeand query system 108. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 1902, the ingestor 252 receives data. The ingestor 252 canreceive the data from one or more forwarders 204, HECs, or othercomponent of the data intake and query system 108. The received data caninclude, but is not limited to, log data or raw machine data, eventsformed from log data, metrics, etc. In some cases, the ingestor 252concurrently receives data from multiple components (e.g., multipleforwarders 204 and/or HECs). As described herein, the forwarders 204 andHECs can obtain the data from a data source 202.

At block 1904, the ingestor 252 generates events from the received data.As described herein, the ingestor 252 can perform a number of operationson the data to generate the events, including, but not limited to,parsing the received data, performing line breaking, merging lines,applying regex rules, extracting timestamps, and punctuation,associating metadata (e.g., host, source, and sourcetype), etc. In somecases, the ingestor 252 can use multiple pipelines of a pipeline set togenerate the events. In certain cases, the ingestor 252 can addgenerated events to a buffer or queue for temporary storage untiladditional processing is to be performed on them.

At block 1906, the ingestor 252 combines multiple events into a group ofevents or grouped events to form a message payload. In some cases, theingestor 252 pulls multiple events from a buffer or queue thattemporarily stores the events to generate the group of events. Theingestor 252 can perform additional processing to prepare the multipleevents for communication to a message bus. This can include encoding orserializing the grouped events and determining a size of the (encoded)grouped events.

In some embodiments, the ingestor 252 groups the events based on theconstraints or capacity of the message bus 254 or message queue 256. Forexample, the message queue 256 may be a third-party provided messagequeue 256 and/or may have a maximum supported message size for messagesor a configured maximum supported message size. Depending on the maximumsupported message size, the ingestor 252 may form the grouped eventsdifferently. For example, with a larger maximum supported message size,the ingestor 252 may create larger groups with more events. For asmaller maximum supported message size, the ingestor 252 may createsmaller groups with fewer events. In certain cases, each group of eventsmay include whole events. In other words, if adding an event to a groupwould cause the group of events to exceed the maximum supported messagesize, the ingestor 252 may exclude the event from the group of eventsrather than attempting to include a portion of the event with the groupof events.

In certain cases, the ingestor 252 may dynamically form grouped eventsdepending on the constraints or capacity of the message queue 256. Forexample, in some cases, the message queue 256 may have a total capacity(e.g., memory capacity or processing capacity, etc.) that can be sharedbetween different messages. Messages of different sizes may usedifferent amounts of the message queue's 256 capacity. In some suchcases, depending on the amount of available capacity (total capacityminus amount of capacity used by messages in the message bus), theingestor 252 can dynamically prepare a group of events for inclusion asa message on the message queue 256. Accordingly, if the availablecapacity at a particular time is large than the group of events may berelatively large, whereas if the available capacity at a particular timeis small, the group of events may be relatively small.

As described herein, the message queue 256 can form part of the messagebus 254 and messages that exceeds the message queue's 256 maximummessage size can be stored on the data store 258. In some such cases,the ingestor 252 may attempt to generate messages that are likely tosatisfy the maximum message size or message size threshold of themessage queue 256. For example, the ingestor 252 may use an average sizeof events to approximate the number of events that can be included in agroup of events and then include that number of events in the group ofevents or message payload and/or track the actual size of each event asit is added to a group of events or message payload and stop addingevents when it determines that adding one more event to the group ofevents will cause the group of events to satisfy or exceed the messagesize threshold. Similarly, the ingestor 252 may use an average size ofencoded or serialized events to approximate and add events to a group ofevents or message payload and/or track the actual size of each eventafter it has been encoded to add events to a group of events or messagepayload.

In some cases, the ingestor 252 only includes complete events in a groupof events or message payload. For example, if adding one additionalevent would cause the ingestor 252 to exceed the message size threshold,the ingestor 252 can omit the additional event from the group of eventsrather than attempting to include a portion of the event in the group ofevents.

At block 1908, the ingestor 252 communicates the grouped events as amessage payload to a message bus 254. As described herein, as part ofcommunicating the grouped events to the message bus 254, the ingestor252 can determine the size of the grouped events or message payload. Ifthe size of the grouped events or message payload satisfies or exceeds asize threshold or maximum message size of the message queue 256, theingestor 252 can send the grouped events to the data store 258 forstorage, obtain a location reference to the grouped events on the datastore 258, and communicate the location reference to the message queue256 for inclusion as a message on the message queue 256.

If the size of the grouped events or message payload does not satisfythe message size threshold or maximum message size of the message queue256, the ingestor 252 can send the grouped events or message payload tothe message queue 256 for inclusion as a message on the message queue256.

Fewer, more, or different blocks can be used as part of the routine1900. In some cases, one or more blocks can be omitted. In someembodiments, the blocks of routine 1900 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 19-23. For example, in some embodiments, the ingestors 252 canmonitor their processing capacity and utilization. Based on adetermination that their utilization satisfies a high utilizationthreshold, the ingestors 252 can request that additional ingestors 252be added to process incoming data. In a similar fashion, if the capacitysatisfies a low utilization threshold, one or more of the ingestors 252can be shut down.

In some cases, rather than the ingestors 252 monitoring their capacityand utilization a separate monitoring component, such as the clustermaster 262, can monitor the capacity and/or utilization of the ingestors252 and scale up or scale down the number of ingestors 252 based on theoverall or individual capacity and/or utilization. Further, as theingestors 252 are separate from the indexers 206, they can be scaled upor scaled down independent of the indexers 206. As such, the number ofcomponents generating events can be dynamically scaled depending on thedemands of the system and can be different from and independent of thenumber of components generating buckets of events, etc.

In certain cases, the ingestor 252 or a monitoring component can trackthe relationship between a received data chunk, events generated fromthe received data, groups of events to which the generated events areadded, and messages to which the generated events are added. As such,once a message is stored to the message bus 254, the ingestor 252 candetermine which events have been stored to the message bus 254. Once allthe events associated with the same data chunk are stored to the messagebus, the ingestor 252 can acknowledge the data chunk to the forwarder204. In response, the forwarder can delete the data chunk of forward theacknowledgement to the data source 202 for deletion, etc.

FIG. 20 is a flow diagram illustrative of an embodiment of a routine2000, implemented by a computing device of a distributed data processingsystem, for communicating groups of events to a message bus 254.Although described as being implemented by the ingestor 252 of the dataintake and query system 108, it will be understood that the elementsoutlined for routine 2000 can be implemented by any one or a combinationof computing devices/components that are associated with the data intakeand query system 108. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 2002, the ingestor forms a group of events. As describedherein, the ingestor 252 can generate the events and place them in abuffer. The events can be generated from raw machine data, metrics, etc.and include raw machine data or metrics associated with a timestamp. Theingestor 252 can then group events from the buffer into groups ofevents. As mentioned, in some cases, the ingestor 252 can group eventsand/or form a message payload based on the constraints and/or capacityof the message queue 256, which may be implemented by a third party.

At block 2004, the ingestor 252 encodes the group of events. In certaincases, the encoding can reduce the size of the data and/or the ingestors252 can compress the data to reduce its size. For example, the ingestormay use zstd or gzip to compress the data or compress the encoded data.In some cases the ingestor 252 uses a schema oriented protocol to encodethe data, such as, but not limited to protobuf, thrift, avro, S2S, etc.In certain cases, the ingestor 252 uses a base64 encoding to encode thedata and/or to encode the data that is to be sent to the message queue256.

At block 2006, the ingestor 252 determines that the size of the encodedgroup satisfies a message size threshold. As described herein, themessage size threshold can be based on the constraints or capacity ofthe message queue 256 and can vary depending on the message queue 256used. For example, as described herein, the message queue 256 may have amaximum message size. In some such cases, the maximum message size (orsome offset from the maximum message size to allow for header and otherdata in the message) can be used as the message size threshold.Accordingly, in determining that the size of the encoded group satisfiesthe size threshold, the ingestor 252 can determine that the size of theencoded group exceeds the maximum message size (or some offset of it).

At block 2008, the ingestor 252 stores the encoded group of events to aremote data store 258. In some cases, the ingestor 252 stores theencoded group of events to the remote data store 258 based on thedetermination that the group encoded group of messages satisfies themessage size threshold. As described herein, the remote data store 258can be a standalone data store and/or part of cloud storage or even theshared storage system 260.

At block 2010, the ingestor 252 obtains a reference to the encodedgroup. The reference can include information about the location of theencoded group of events in the remote data store. In some cases, theingestor 252 can receive the reference to the encoded group from theremote data store 258 as part of storing the encoded group on the remotedata store 258.

At block 2012, the ingestor communicates the reference to a messagequeue 256 as part of a message. As described herein, by communicatingthe reference to the message queue 256 instead of the encoded group, thesize of the message for the message queue 256 can be smaller and stayunder the maximum message size or message size threshold of the messagequeue 256. Further, as described herein, an indexer 206 can retrieve themessage that include the reference from the message queue 256 and usethe reference to obtain the encoded events from the remote data store258. In this way, the ingestor 252 can send larger message to theindexers 206 while satisfying the constraints of the message queue 256.

Fewer, more, or different blocks can be used as part of the routine2000. In some cases, one or more blocks can be omitted. In someembodiments, the blocks of routine 2000 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 19, and/or 21-23. For example, in some embodiments, the events maynot be encoded before determining their size and/or storing them to thedata store 258. In other cases, the ingestor 252 may determine that theencoded (or decoded) group of events do not satisfy the message sizethreshold. In some such cases, the ingestor 252 may communicate thegroup of events to the message queue 256 as part of a message and mayexclude blocks 2006-2010.

In addition, as described herein at least with reference to FIG. 19, theingestors can send an acknowledgement to a forwarder 204 or other sourceonce events associated with a data chunk received from the source havebeen saved to the message bus. Further, as described herein at leastwith reference to FIG. 19, the ingestors 252 (or a monitoring component)can monitor the ingestors 252 and scale up or scale down the number ofingestors 252 independent of the number of indexer 206.

3.3. Indexer Data Flow Example

FIG. 21 is a data flow diagram illustrating an embodiment of the dataflow and communications between a variety of the components of adistributed data processing system, such as the data intake and querysystem 108 to store aggregate slices and buckets in the shared storagesystem 260. The data flow diagram of FIG. 21 illustrates an example ofdata flow and communications between a message bus 254, indexer 206, andshared storage system 260. However, it will be understood, that in someof embodiments, one or more of the functions described herein withrespect to FIG. 21 can be omitted, performed concurrently or in adifferent order and/or performed by a different component of the dataintake and query system 108. In addition, not all communications betweencomponents may be illustrated. For example, as part of communicatinginformation about storing the aggregate slices to the shared storagesystem 260 and rolling the buckets to the shared storage system 260, theindexer 206 can notify a monitoring component, such as the clustermaster 262. In addition, the cluster master 262 can coordinate or beinvolved in the deletion of relevant aggregate slices from the sharedstorage system 260.

At (1A), the message bus 254 processes messages related to groups ofevents from the ingestors 252, as described in greater detail withreference to (5) of FIG. 18.

At (1B), the indexer 206 monitors its capacity. As described herein theindexers 206 can monitor their own usage, including, but not limited toCPU usage, memory use, error rate, network bandwidth, networkthroughput, time taken to process the data, time taken to schedule andexecute a job or pipeline, the number of events, slices, and bucketsthat it is currently processing, etc. In addition, the indexer 206 candetermine the processing requirements for each new message or group ofevents. In some cases, the indexer 206 can provide metrics to anothercomponents, such as the cluster master 262, or other component. Thecomponent that receives the metrics from the indexer 206 can determinethe capacity of the indexer 206.

At (2), the indexer 206 requests and receives a message from the messagebus 254. As described herein, the message (or message payload) can comefrom the message queue 256 in the form of a group of events or areference to a group of events stored in the data store 258, or themessage (or message payload) can come from the data store 258 as a groupof events.

In some cases, the indexer 206 requests the message based on adetermination that it has the capacity to process an additional message.In certain cases, the indexer 206 can request multiple messagesconcurrently. The frequency and number of messages requested can dependon the determined capacity of the indexer 206. For example, based on thecurrent CPU and memory usage and an estimation of the amount ofprocessing required to process a message, the indexer 206A may, onaverage, request one message every five seconds and the indexer 206Bmay, on average, request three messages every ten seconds. As theavailable capacity for a particular indexer 206 decreases it can requestmessages less frequently or wait until additional capacity becomesavailable. In this way, the indexers 206 can asynchronously request,download, and process messages and events from the message bus 254.

By relying on a pull-based system to process groups of events, the dataintake and query system 108 can more effectively distribute the eventprocessing to the indexers 206 that are best suited to handle it. Thus,heterogeneous indexers 206 (e.g., indexers 206 with different hardwarecapacity or assigned capacity) can process the data at different rates.For example, indexers 206 with more processing power (e.g., moreprocessor cores, memory, etc.) can process more events than indexers 206with less processing power because they are able to process more eventsconcurrently or able to process the events faster. Similarly, if anindexer 206 gets stuck processing a large number of events from a givenmessage, it will simply not ask for additional messages. As such, slowerprocessing of the given message by the indexer 206 will not inhibit theprocessing of other messages by other indexers 206. In this way, thedata intake and query system can improve the throughput of the indexers206 as a whole.

At (3), the indexer 206 processes the events related to the message. Asdescribed herein, the events related to the message can come from themessage queue 256 or from the data store 258. As part of processing theevents, the indexer 206 can add the events to hot buckets and editableslices associated with hot buckets. In addition, the indexer 206 can,based on a slice rollover policy, convert an editable slice to anon-editable slice and add it to an aggregate slice that is associatedwith the same bucket as the editable slice. The indexer 206 can do thisfor each editable slice that it is processing based on the slicerollover policy. Upon converting an editable slice associated with abucket to a non-editable slice, the indexer 206 can generate a neweditable slice associated with the bucket.

At (4) the indexer 206 stores (or initiates storage of) an aggregateslice to the shared storage system 260. In certain cases, the aggregateslice is compressed before it is stored to the shared storage system260. In some cases, the indexer 206 stores the aggregate slice to theshared storage system 260 based on an aggregate slice backup policy. Asdescribed herein, the aggregate slice backup policy can indicate when anaggregate slice is to be saved to the shared storage system 260 (e.g.,based on the size of the aggregate slice satisfying or exceeding anaggregate slice size threshold and/or the amount of time since theaggregate slice was opened satisfying or exceeding an aggregate slicetime threshold). Once the indexer 206 determines that the aggregateslice is to be stored to the shared storage system 260, it can begin theupload and/or flag or mark the aggregate slice for upload. In certaincases, before storing the aggregate slice the shared storage system 260,the indexer 206 can determine whether the bucket associated with theaggregate slice has been or is being uploaded to the shared storagesystem. If the indexer 206 determines that the associated bucket hasbeen or is being uploaded to the share shared storage system 260, theindexer 206 can determine that it will not upload the aggregate slice tothe shared storage system 260 and/or terminate any upload (e.g., unmarkor unflag the aggregate slice, delete the aggregate slice, etc.). Insome cases, the indexer 206 can determine that the associated bucket hasbeen uploaded based on an absence of a bucket ID on the indexer 206. Incertain cases, the indexer 206 can determine that the associated bucketis being upload based on a flag or marking of the bucket in the indexer206. In certain cases, the indexer 206 can terminate an upload based ona determination that a particular indexer 206 is to be shut down or aspart of a time out associated with the shutdown of the particularindexer.

In some cases, the indexer 206 can upload slices of the aggregate slicein a data offset or logical offset order. For example, if the aggregateslice includes a first slice from the logical offset 0-500, a secondslice from logical offset 501-2000, and a third slice from logicaloffset 2001-3100, the indexer 206 upload and store the first slice (andreceive an acknowledgement) before beginning the upload of the secondslice, and so on. In this way, if there are any issues with uploadingthe slices, the indexer 260 can provide a guarantee that if the thirdslice was uploaded then the first and second slices should also exist inthe shared storage system 260. As such, in the event a restore isstarted (e.g., because the indexer 206 failed), the system 108 candetermine which slices are available to restore the lost data or bucket.

In certain cases, the indexer 206 can notify a monitoring component,such as the cluster master 262 which aggregate slice has been uploadedto the shared storage system 260. If the indexer 206 fails, the clustermaster 262 can provide the information about the aggregate slice to anew indexer 206.

At (5), the indexer 206 converts a hot bucket to a warm bucket andstores a copy of the warm bucket to the shared storage system 260. Asdescribed herein, the indexer 206 can convert a hot bucket to a warmbucket based on a bucket rollover policy. As mentioned, the bucketrollover policy can indicate when a bucket (e.g., based on size of thebucket satisfying or exceeding a bucket size threshold, or the timesince the bucket was created satisfying or exceeding a bucket timingthreshold, etc.) is to be converted from a hot bucket to a warm bucketand stored in the shared storage system 260. In some cases as part ofstoring the copy of the warm bucket to the shared storage system 260,the indexer 206 can mark or flag the warm bucket for upload. In certaincases, the indexer 206 can use the flag or marking to identifyassociated aggregate slices and/or hot slices that are not to be uploador are to be deleted. By storing a copy of the warm bucket to the sharedstorage system 260, the indexer 206 can improve the resiliency of thedata in the data intake and query system. For example, if the indexer206 fails, then the cluster master 262 can assign another indexer 206 tomanage and/or search the bucket. In some cases, the entire warm bucketis stored to the shared storage system 260. In certain cases, a portionof the warm bucket is stored to the shared storage system 260. Forexample, metadata files or indexes may not be stored in the sharedstorage system 260 as part of the bucket. In some such cases, theaggregate slices may be stored with a bucket identifier indicating thatthey are part of the same bucket. In such cases, if the bucket is to berestored, an indexer 206 that restores the bucket can download theaggregate slices and recreate the bucket (e.g., recreate the indexes,metadata files, or other files that were not stored as part of thebucket.

At (6) the indexer 206 acknowledges to the message bus 254 events thathave been stored to the shared storage system 260. As the indexer 206stores aggregate slices and buckets in the shared storage system 260, itcan track which events were stored in the shared storage system 260 andfrom which message bus 254 message the events originated. As such, theindexer 206 can determine when all of the events from a particularmessage have been stored to the shared storage system 260 as part of anaggregate slice or as part of a bucket. In some cases, once all of theevents from a particular message have been stored to the shared storagesystem 260 (as part of an aggregate slice or a bucket), the indexer 206can acknowledge the relevant message to the message bus 254.

At (7), the message bus 254 purges the acknowledged messages andcorresponding events from the message bus. In some cases, this caninclude deleting the message that includes the events from the messagequeue 256, deleting the message that includes a reference to the eventsfrom the message queue 256, and/or deleting the relevant group of eventsfrom the data store 258.

At (8), the shared storage system 260 deletes the aggregate slices thatcorrespond to the rolled bucket. In some cases, the indexer 206, clustermaster 262, or other component of the data intake and query system 108can track the relationship between aggregate slices and buckets. When abucket is stored to the shared storage system 260, the relevantcomponent can have the shared storage system 260 delete the aggregateslices associated with the bucket. As described herein, the aggregateslices that are deleted can include the same events or a subset of theevents in a bucket. Accordingly, once the bucket is uploaded to theshared storage system 260, the aggregate slices that were uploadedbefore the bucket can be deleted. As mentioned previously, the indexer206 can monitor the storage of a bucket to the shared storage system260. Any active or aggregate slices associated with the bucket beinguploaded or uploaded bucket can be deleted, and any uploads of suchslices can be terminated.

Fewer more or different functions can be performed by the differentcomponents of the data intake and query system 108. In some cases, anindexer 206 can inform the message bus 254, cluster master 262, or othermonitoring component of the data intake and query system 108, each timean event has been stored. In some such cases, the monitoring componentcan determine when all events from a message have been stored to theshared storage system 260 and initiate the acknowledgement to themessage bus 254 and/or initiate the purging of the relevant message andevents from the message bus 254.

In addition any one or any combination of the aforementioned processescan be performed concurrently. For example, the (1A) and (1B) may beperformed concurrently. Similarly, (4), (5), or (6) may be performedconcurrently, etc.

Further, it will be understood that the functions described herein canbe performed concurrently for multiple events, messages, slices,aggregate slices, and buckets. Accordingly, in some embodiments, anindexer 206 can concurrently assign different events to different hotslices and buckets, convert multiple hot slices to non-editable slicesand add them to different aggregate slices, store multiple aggregateslices to the shared storage system 260, roll multiple hot buckets towarm buckets, and store multiple warm buckets to the shared storagesystem. It will further be understood that multiple indexers 206 can beconcurrently performing these functions for different data.

3.4. Indexer Flow Examples

FIG. 22 is a flow diagram illustrative of an embodiment of a routine2200, implemented by a computing device of a distributed data processingsystem, for storing aggregate data slices to a shared storage system.Although described as being implemented by the indexer 206 of the dataintake and query system 108, it will be understood that the elementsoutlined for routine 2200 can be implemented by any one or a combinationof computing devices/components that are associated with the data intakeand query system 108. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 2202, the indexer 206 obtains a message payload from a messagebus 254. As described herein, the message bus can include a messagequeue 256 and a data store 258. In some cases, the message queue 256 canbe a third-party provided message queue 256 and the data store can bepart of cloud storage. As described herein, the indexer 206 can obtainthe message payload from the message queue 256 or the data store 258.The message payload can include a group of events, where each eventincludes raw machine data or metrics associated with a timestamp.

In certain cases, the indexer 206 can obtain two message payload fromthe message bus 254 for the same transaction or group of events. In somesuch cases, the indexer 206 can obtain a first message payload from themessage queue 256 and a second message payload from the data store 258.The first message payload can include a reference to the second messagepayload and the second message payload can include the group of events.

At block 2204, the indexer 206 extracts the group of events from themessage payload. In some cases, as part of extracting the group ofevents from the message payload, the indexer 206 can decode the group ofevents.

At block 2206, the indexer 206 adds events to one or more data slices.As described herein, the indexer 206 can add events to hot or editabledata slices. In some cases, the events can be added to hot data slicesassociated with different buckets and/or indexes such that events thatare associated with the same bucket or index are assigned to the samehot slice. In some cases, if there is no hot slice for a particularindex or bucket with which an event is associated, the indexer 206 cangenerate a hot slice. In addition to adding the events to one or moredata slices, the indexer 206 can add the events to buckets. Similar tothe data slices, the indexer 206 can add the events to buckets based onan index associated with the event and bucket such that eventsassociated with the same index are assigned to the same bucket.

At block 2208, the indexer 206 converts the hot slice to a warm ornon-editable slice and adds the slice to an aggregate slice based on ahot slice rollover policy. As described herein, the hot slice rolloverpolicy can indicate that a particular hot slice is to be converted to anon-editable slice based on one or more hot slice size thresholds and/orhot slice timing thresholds. For example, once the hot slice reaches aparticular size (satisfies the hot slice size threshold) or after a setamount of time since the hot slice was created (satisfies the host slicetiming threshold), it can be converted to a non-editable slice and addedto an aggregate slice. When a hot slice is converted to a non-editableslice, the indexer 206 can create a new hot slice for the next event (orwait until another relevant is received). In some cases, if no aggregateslice is available for a particular bucket, the indexer 206 can createan aggregate slice and add the non-editable slice to the newly createdaggregate slice. In certain cases, the indexer 206 can create anaggregate slice at the same time that it creates a hot slice for aparticular bucket (if an aggregate slice does not already exist). Insome cases, as part of adding the non-editable slice to the aggregateslice, the indexer 206 can compress the slice, thereby reducing theamount of memory used to store the data of the slice.

At block 2210, based on an aggregate slice backup policy, the indexer206 initiates storage of (or stores) a copy of the aggregate slice tothe shared storage system 260. As described herein, the aggregate slicebackup policy can indicate that a particular aggregate slice is to bestored in the shared storage system 260 based on one or more sizethresholds and/or timing thresholds. For example, once an aggregateslice reaches a particular size, has a particular number ofwarm/non-editable slices added to it, or after a particular amount oftime, it can be stored in the shared storage system 260. In some casesas part of initiating storage of the aggregate slice, the indexer 206flags or marks the aggregate slice for upload. In certain cases, uponinitiating storage of the aggregate slice, the indexer 206 determineswhether a bucket associated with the aggregate slice has been uploaded,is being uploaded, or has been flagged or marked for upload. In theevent, the indexer 206 determines that the bucket has been uploaded, isbeing uploaded, or has been flagged or marked for upload, the indexercan terminate the storage of the aggregate slice to the shared storagesystem 260.

Fewer, more, different blocks can be added to the routine 2200. Forexample, the indexer 206 can continuously request messages from themessage bus 254, concurrently request multiple message associated withdifferent events, etc. In some embodiments, the blocks of routine 2200can be combined with any one or any combination of blocks describedherein with reference to at least FIGS. 19-21, and/or 23. As describedherein, in certain cases, the indexer 206 can track the relationshipbetween messages, aggregate slice and/or buckets. Once all of the eventsassociated with a particular message have been stored to the sharedstorage system 260, the indexer 206 can communicate an acknowledgementto the message bus 254. In turn, the message bus can purge the message.

In some cases, based on a bucket rollover policy, the indexer 206 rollsa bucket to the shared storage system 260 that corresponds to theaggregate slice. As described herein, each aggregate slice can beassociated with a particular bucket and a bucket may be associated withmultiple aggregate slices. As further described herein, the bucketrollover policy can indicate that a hot bucket is to be converted to awarm bucket and stored in the shared storage system 260 based on one ormore size thresholds and/or timing thresholds. For example, once a hotbucket reaches a particular size, includes a particular number ofaggregate slices or events, or after a particular amount of time, it canbe converted to a warm bucket and stored in the shared storage system260.

In addition, as part of the bucket rollover policy when a warm bucket isstored to the shared storage system 260, the aggregate slices associatedwith the warm bucket that were stored previously can be deleted from theshared storage system 260. In some embodiments, the indexer 206, clustermaster 262, or other monitoring component can track which slices areassociated with which buckets and communicate with the shared storagesystem 260 to delete the relevant aggregate slices once thecorresponding bucket is stored in the shared storage system 260.

In certain cases, as part of storing the warm bucket to the sharedstorage system 260, hot slices and aggregate slices on the indexer 206that are associated with the warm bucket can be deleted and/or removed.

FIG. 23 is a flow diagram illustrative of an embodiment of a routine2300, implemented by a computing device of a distributed data processingsystem, for asynchronously obtaining and processing a message payloadfrom a message bus 254. Although described as being implemented by theindexer 206 of the data intake and query system 108, it will beunderstood that the elements outlined for routine 2300 can beimplemented by any one or a combination of computing devices/componentsthat are associated with the data intake and query system 108. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 2302, the indexer 206 monitors metrics of the indexer 206. Asdescribed herein, the indexer 206, cluster master 262, and/or othermonitoring component can monitor one or more metrics of the indexer 206,such as, but not limited to, CPU usage, memory use, error rate, networkbandwidth, network throughput, time taken to process the data, timetaken to schedule and execute a job or pipeline, the number of events,slices, and buckets that it is currently processing, time to download amessage, time to decode a message, time to purge a message or send anacknowledgement, and/or time to renew messages if used or needed, etc.

At block 2304, the indexer 206 determines that the indexer 206 satisfiesa capacity threshold. As described herein, determining that the indexer206 satisfies a capacity threshold can be based on the metrics that arebeing monitored. For example, the indexer 206 can compare the CPU usage,available memory, or other computer resources with an estimate of theamount of CPU and/or memory used to process a new message. Similarly,any one or any combination of the aforementioned metrics can be comparedwith a threshold and/or combined and compared with a respectivethreshold or threshold to determine if the indexer satisfies thecapacity threshold. Based on a determination that the indexer 206includes sufficient CPU and memory to process at least one additionalmessage, the indexer 206 can determine that the indexer 206 satisfiesthe capacity threshold.

At block 2306, the indexer 206 requests (and receives) a message payloadfrom the message bus 254 based on the determination that it hassufficient capacity. As described herein, a message payload can includea group of events or a reference to a location in a data store 258 fromwhich the group of events can be retrieved. In some cases, depending onthe amount of computer resources available, the indexer 206 can requestmultiple payloads messages simultaneously or concurrently. For example,if the indexer 206 has capacity to process three messages, it canrequest three messages at the same time.

At block 2308, the indexer 206 extracts events from the message payload,similar to block 2204 of FIG. 22.

At block 2310, the indexer 206 adds the events to one or more buckets.As described herein, each event can be added to a particular bucket. Insome cases, events associated with the same index can be assigned to thesame bucket.

At block 2312, the indexer 206, stores the one or more buckets to ashared storage system. As described herein, at least with reference toFIG. 22, based on a bucket rollover policy, buckets can be convertedfrom editable buckets to warm buckets and stored in a shared storagesystem 260. In addition, as part of the bucket rollover policy,aggregate slices associated with the stored bucket can be deleted fromthe shared storage system 260 and/or the indexer 206. Hot slicesassociated with the bucket can also be deleted from the indexer 206. Inaddition, when a bucket is converted to a non-editable bucket, theindexer 206 can generate a new bucket. The new bucket can be associatedwith the same index as the rolled bucket.

Fewer, more, different blocks can be added to the routine 2300. Forexample, multiple indexers 206 can concurrently request messages fromthe message bus 254. By having indexers 206 monitor their availabilityand request messages based on their availability, the messages can bedownloaded and processed asynchronously. Further, by using a pull-basedscheme to retrieve and process messages and events, data intake querysystem can improve load balancing between indexers 206. In someembodiments, the blocks of routine 2300 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 19-22.

As described herein, in some cases, a monitoring component or theindexers 206 can monitor the indexers' 206 utilization. Based on theutilization, one or more indexers 206 can be shut down to improveefficiency and utilization or instantiated to improve throughput. Asdescribed herein, the increasing or decreasing of the indexers 206 canbe done independent of the number of ingestors 252. Further, there maybe a different number of indexers 206 than ingestors 252.

4.0. USING A CLUSTER MASTER AND BUCKET MAP IDENTIFIERS TO MANAGE DATA

As described herein, the data intake and query system 108 can use acluster master 262 and/or bucket map identifiers to store and recoverdata.

4.1. Recovering Pre-Indexed Data Following a Failed Indexer

As described herein, the data intake and query system 108 can indexlarge amounts of data using one or more indexers 206. In some cases, anindexer 206 can store a copy of the data that the indexer 206 isassigned to process in the shared storage system 260, and the clustermaster 262 can track the storage of the data and the indexer 206assigned to process the data. By storing the data in shared storagesystem, the indexers 206 can improve data availability and resiliency.For example, in the event an indexer 206 fails or is otherwise unable toindex data that it has been assigned to index, the cluster master 262can assign a second indexer 206 to process the data. In some such cases,the second indexer 206 can download the data from the shared storagesystem 260. In this way, the data intake and query system 108 candecrease the likelihood that data will be lost as it is processed by theindexers 206.

FIG. 24 is a data flow diagram illustrating an embodiment of the dataflow and communications between a variety of the components of adistributed data processing system, such as the data intake and querysystem 108 to recover pre-indexed data from a shared storage systemfollowing a failed indexer 206. The data flow diagram of FIG. 24illustrates an example of data flow and communications between a firstindexer 206A, a second indexer 206B, a cluster master 262, and a sharedstorage system 260. However, it will be understood, that in some ofembodiments, one or more of the functions described herein with respectto FIG. 24 can be omitted, performed concurrently or in a differentorder and/or performed by a different component of the data intake andquery system 108. Accordingly, the illustrated embodiment anddescription should not be construed as limiting.

At (1), the first indexer 206A receives a first set of one or moregroups of data for processing. In the illustrated embodiment, the groupsof data can correspond to slices of data to be processed by the indexer206. A group of data can include one or more data records. A data recordcan include data or a reference to a location at which the data islocated. Data in a data record (or in a location referenced by the datarecord) can include any one or any combination of: raw machine data,structured data, unstructured data, performance metrics data,correlation data, data files, directories of files, data sent over anetwork, event logs, registries, JSON blobs, XML data, data in a datamodel, report data, tabular data, messages published to streaming datasources, data exposed in an API, data in a relational database, sensordata, image data, or video data, etc.

At (2), first indexer 206A stores the first set of one or more groups ofdata. In some cases, the first indexer 206A can store the first set ofone or more groups of data prior to processing it. In some cases, thefirst set of one or more groups of data are slices of data associatedwith an editable bucket, also referred to herein as a hot bucket. Thefirst indexer 206A can store the first set of one or more groups of datain local storage (for example, in the data store 208A). In addition oralternatively, the first indexer 206A can store the first set of one ormore groups of data in shared storage system 260. In some cases, thefirst indexer 206A stores the first set of one or more groups of databoth locally and in shared storage system 260. In this way, the firstindexer 206A can locally process the first set of one or more groups ofdata. However, should the first indexer 206A fail or otherwise becomeunavailable prior to processing the first set of one or more groups ofdata, an available indexer can be assigned to process at least a portionof the first set of one or more groups of data in place of the firstindexer 206A, and the reassigned available indexer can retrieve thefirst set of one or more groups of data from its location in sharedstorage system 260.

In some cases, as part of storing the first set of one or more groups ofdata to shared storage system 216, the first indexer 206A can verify orobtain acknowledgements that the first set of one or more groups of datawas stored successfully. In some embodiments, the first indexer 206A candetermine information regarding the first set of one or more groups ofdata stored in the shared storage system 216. For example, theinformation can include location information regarding the first set ofone or more groups of data that was stored to the shared storage system216 or one or more data identifiers related to the first set of one ormore groups of data that was copied to shared storage system 216.

At (3), the first indexer 206A communicates information regarding thefirst set of one or more groups of data stored in shared storage system216 to the cluster master 262 and/or the cluster data store 264. Forexample, the first indexer 206A can communicate a first set of one ormore data identifiers that are associated with the first set of one ormore groups of data of the data, the location of the first set of one ormore groups of data in shared storage system 216, etc. In this way, thecluster master 262 and/or the cluster data store 264 can be keptup-to-date with the contents of the shared storage system 216 and anindication of the indexer 206 that is responsible for processingdifferent groups of data.

In some cases, the first indexer 206A can also provide information aboutthe first set of one or more groups of data. As described herein, thefirst set of one or more groups of data can include one or more groupsof data, and a group of data can include one or more data records. Agroup of data, or a data record, can include data from, or otherwise beassociated with, indexes, sources, sourcetypes, hosts, users, etc. Insome such cases, the information provided by the first indexer 206A tothe cluster master 312 can include, but is not limited to, a combinationof any one or more of an index identifier identifying an indexassociated with one or more groups of data, a source identifieridentifying a source associated with one or more groups of data, asourcetype identifier identifying a sourcetype associated with one ormore groups of data, a host identifier identifying a host associatedwith one or more groups of data, a user identifier identifying a userassociated with one or more groups of data, an indexer identifieridentifying the indexer assigned to process one or more groups of data,etc. In addition or alternatively, the first set of one or more dataidentifiers can include a timestamp or time range associated with thefirst set of one or more groups of data, such as a timestamp or timerange associated with a data record, group of data, set of one or moregroups of data, or bucket. For example, the first set of one or moredata identifiers can include an indication of an earliest or latest timeassociated with a data record, group of data, set of one or more groupsof data, or bucket. Other information that could be used as filtercriteria in association with the first set of one or more groups of datacan be included as desired. In this way, the cluster master 312 canretain sufficient information to identify groups of data that are to besearched based on filter criteria received in a search.

The first location information can include a reference to a location inshared storage system 260 at which the first set of one or more groupsof data was stored or a location in the shared storage system 260 thatfirst set of one or more groups of data can be retrieved.

In response to receiving the communication from the first indexer 206A,the cluster master 262 can update the cluster data store 264 to identifythat the first set of one or more groups of data has been sent to thefirst indexer 206A for processing, but has not yet been processed.Further, the cluster master 262 can update the cluster data store 264 toidentify the location at which the first set of one or more groups ofdata has been stored in the shared storage system 260.

At (4), the first indexer 206A processes the first set of one or moregroups of data. In some embodiments, the first indexer 206A processesthe first set of one or more groups of data (or the data obtained usingthe first set of one or more groups of data) and stores it in buckets.As part of the processing, the first indexer 206A can determineinformation about the first set of one or more groups of data (forexample, host, source, sourcetype), extract or identify timestamps,associated metadata fields with the first set of one or more groups ofdata, extract keywords, transform the first set of one or more groups ofdata, identify and organize the first set of one or more groups of datainto events having raw machine data associated with a timestamp, etc. Insome embodiments, the first indexer 206A uses one or more configurationfiles and/or extraction rules to extract information from the events orthe first set of one or more groups of data. In some cases, as part ofthe processing, the first indexer 206A can generate one or more indexesassociated with the buckets, such as, but not limited to, one or moreinverted indexes, TSIDXs, keyword indexes, etc. The first set of one ormore groups of data and the indexes can be stored in one or more filesof the buckets. In addition, first indexer 206A can generate additionalfiles for the buckets, such as, but not limited to, one or more filterfiles, a bucket summary, or manifest, etc.

At (5), the first indexer 206A stores results of the processing at (4).Similar to storing the first set of one or more groups of data at (3),the first indexer 206A can store the results in local storage (forexample, in the data store 208A) and/or in shared storage system 260. Insome cases, the first indexer 206A stores the results both locally andin shared storage system 260. In this way, should the first indexer 206Aremain available, it can be utilized to execute at least a portion ofone or more queries on the results. However, should the first indexer206A fail or otherwise become unavailable, an available indexer can beassigned to execute the at least a portion of the one or more queries,and the reassigned available indexer can retrieve the results from itslocation in shared storage system 260.

In some cases, as part of storing the results to shared storage system216, the first indexer 206A can verify or obtain acknowledgements thatthe results were stored successfully. In some embodiments, the firstindexer 206A can determine information regarding the results stored inthe shared storage system 216. For example, the information can includelocation information regarding the results that were stored to theshared storage system 216 or one or more data identifiers related to theresults that were copied to shared storage system 216.

In some cases, the results are stored in or as one or more buckets, andthe one or more buckets are copied to the shared storage system 216. Asdescribed herein, the buckets in the data store 208 that are no longeredited by first indexer 206A (e.g., bucket that include data that hasbeen processed) can be referred to as warm buckets or non-editablebuckets. In some embodiments, once first indexer 206A determines that ahot bucket is to be copied to storage system 260, it can convert the hot(editable) bucket to a warm (non-editable) bucket, and then move or copythe warm bucket to the shared storage system 260.

At (6), the first indexer 206A communicates information regarding theresults stored in shared storage system 216 to the cluster master 262and/or the cluster data store 264. For example, the first indexer 206Acan communicate an indication that the first set of the one or moregroups of data was processed, location information identifying thelocation of the first results in shared storage system 216, etc. In somecases, an indication that the first set of the one or more groups ofdata was processed can include location information indicating alocation in shared storage system 260 at which the first results werestored. For example, the cluster master 262 can determine that the firstindexer 206A processed the first set of one or more groups of data basedon its receipt of location information.

In response to receiving the communication from the first indexer 206A,the cluster master 262 can update the cluster data store 264 to identifythat the first set of one or more groups of data has been processed.Further, the cluster master 262 can update the cluster data store 264 toidentify the location at which the first results have been stored in theshared storage system 260.

At (7), the cluster master 262 deletes the first set of one or moregroups of data from the shared storage system 260. For example, once thefirst results have been stored in shared storage system 260, the clustermaster 262 can delete the corresponding first set of the one or moregroups of data that it stored in the shared storage system 260. As anon-limiting example, of the first set of one or more groups of datainclude slices of a hot bucket and the first results include a warmbucket that corresponds to the hot bucket, the cluster master 262 candelete the slices of the hot bucket from the shared storage system 260based on an indication that the corresponding warm bucket has beenstored in the shared storage system 260. By removing the first set ofthe one or more groups of data from the shared storage system 260, thecluster master 262 can free up additional space in the shared storagesystem 260. In some cases, the cluster master 262 can update the clusterdata store 264 to reflect that the first set of one or more groups ofdata has been deleted or removed from the shared storage system 260.

At (8), the first indexer 206A receives a second set of one or moregroups of data. At (9), the first indexer 206A stores the second set ofone or more groups of data. And at (10), the first indexer 206Acommunicates information regarding the second set of one or more groupsof data stored in the shared storage system 260. The interactions, (8),(9), and (10), are similar to interactions (4), (5), (6), and (8)discussed above, and therefore will not be re-described.

At (11), the cluster master 262 determines that the first indexer 206Adid not process the second set of one or more groups of data. Asdescribed herein, the cluster master 262 monitors the indexers 206(including the first indexer 206A) of the data intake and query system108. Monitoring the indexers 206 can include requesting and/or receivingstatus information from the indexers 206. In some embodiments, thecluster master 262 passively receives status information from theindexers 206 without explicitly requesting the information. For example,the indexers 206 can be configured to periodically send status updatesto the cluster master 262. In certain embodiments, the cluster master262 receives status information in response to requests made by thecluster master 262. In some cases, the cluster master 262 can determinethat the first indexer 206A did not process the second set of one ormore groups of data based on the status information communications orabsence of communications or “heartbeats” from the first indexer 206A.

In some cases, the cluster master 262 can determine that the firstindexer 206A did not process the second set of one or more groups ofdata based on a determination that the first indexer 206A is unavailableor failing. For example, in some cases, the cluster master 262 candetermine that the first indexer 206A is unavailable if one or moremetrics associated with the first indexer 206A satisfies a metricsthreshold. For example, the cluster master 262 can determine that thefirst indexer 206A is unavailable if a utilization rate of the firstindexer 206A satisfies a utilization rate threshold and/or if an amountof available memory available to the first indexer 206A satisfies amemory threshold. As another example, the cluster master 262 candetermine that the first indexer 206A is unavailable if an amount ofavailable processing resources of the first indexer 206A satisfies aprocessing resources threshold. As a corollary, in some cases, thecluster master 262 can determine that the first indexer 206A isavailable based on a determination that one or more metrics associatedwith the first indexer 206A does not satisfy a metrics threshold.

In the event an assigned indexer (in this example, the first indexer206A) becomes unresponsive or unavailable during the processing of thedata to which it is assigned, the cluster master 262 can re-assign dataof the unavailable indexer to one or more available indexers.Accordingly, the data intake and query system 315 can quickly recoverfrom an unavailable or unresponsive component without data loss andwhile reducing or minimizing delay. In this example, the first indexer206A is determined to have become unresponsive or unavailable.

At (12), the cluster master 262 receives a status update communicationfrom a second indexer 206B, thereby indicating that the second indexer206B is available for processing. Based at least in part on adetermination that the second indexer 206B is available for processing,at (13), the cluster master 262 assigns the second indexer 206B toprocess the second set of one or more groups of data. In some cases, thesecond indexer 206B is assigned to process only a portion of the secondset of one or more groups of data. For example, the cluster master 262may distribute the processing of the second set of one or more groups ofdata among multiple available indexers 206 and/or the cluster master 262may determine that the first indexer 206A processed some portion of thesecond set of one or more groups of data.

At (14), the second indexer 206B processes the second set of one or moregroups of data to provide second results. At (15), the second indexer206A stores the second results. At (16), the second indexer 206Acommunicates information regarding the second results stored in theshared storage system 260. And at (17), the cluster master 262 deletesthe second set of the one or more groups of data from shared storagesystem 260. The interactions (14), (15), (16), and (17), are similar tointeractions (4), (5), (6), and (8) discussed above, and therefore willnot be re-described.

In certain embodiments, (1)-(7) may be omitted. For example, in somesuch embodiments, the data flow diagram of FIG. 24 can include onlythose steps relating to the failure of the first indexer 206A and therecovery of the second set of one or more groups of data from the sharedstorage system 260. For example, in some cases, the first indexer 206Ais not assigned/does not receive the first set of one or more groups ofdata to process.

FIG. 25 is a flow diagram illustrative of an embodiment of a routine2400, implemented by a computing device of a distributed data processingsystem, recovering pre-indexed data from a shared storage systemfollowing a failed indexer. Although described as being implemented bythe cluster master 262 of the data intake and query system 108, it willbe understood that the elements outlined for routine 2400 can beimplemented by one or more computing devices/components that areassociated with the data intake and query system 108, such as, but notlimited to, the cluster data store 264, the search head 210, the sharedstorage system 260, the indexer 206, etc. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 2502, the cluster master 262 receives a data identifier from afirst indexer 206A. As described, the data identifier can identify, orbe associated with, a set of one or more groups of data that the firstindexer 206A is assigned to process. In some cases, the one or moregroups of data can correspond to one or more slices of data of a hotbucket being processed by the first indexer 206A.

In some cases, the set of one or more groups of data includes a singlegroup of data. In some cases, the set of one or more groups of dataincludes more than one group of data. As described, a group of data caninclude one or more data records. A data record can include data or areference location at which the data is located. Data in a data record(or in a location referenced by the data record) can include any one orany combination of: raw machine data, structured data, unstructureddata, performance metrics data, correlation data, data files,directories of files, data sent over a network, event logs, registries,JSON blobs, XML data, data in a data model, report data, tabular data,messages published to streaming data sources, data exposed in an API,data in a relational database, sensor data, image data, or video data,etc.

At block 2504, the cluster master 262 receives location information fromthe first indexer 206A. As described herein, the location informationcan include a reference to a first location in shared storage system260. The first location can be the first location in shared storagesystem 260 at which the set of one or more groups of data was stored.

At block 2506, the cluster master 262 determines that the first indexer206A did not process the set of one or more groups of data. The clustermaster 262 can determine whether the first indexer 206A processed theset of one or more groups of data using any combination of varioustechniques described herein. For example, the cluster master 262 candetermine that the first indexer 206A did not process the set of one ormore groups of data based on status update communications or absencethereof.

At block 2508, the cluster master 262 assigns a second indexer 206B toprocess the set of one or more groups of data. In some cases, assigningthe second indexer 206B to process the set of one or more groups of dataincludes communicating an indication of at least one of the firstlocation or the data identifier to the second indexer 206B. In someembodiments, the cluster master 262 assigns the second indexer 206Bbased on a determination that the second indexer 206B is available. Incertain embodiments, the cluster master 262 assigns the second indexer206B to process a portion of the set of one or more groups of data andassigns other indexers 206 to process other portions.

At block 2510, the cluster master 262 receives an indication that thesecond indexer 206B has successfully processed the set of one or moregroups of data. In some cases, to successfully process the set of one ormore groups of data, the second indexer 206B obtains or downloads theset of one or more groups of data from the first location, processes theset of one or more groups of data to provide results, and uploads theresults to a second location in the shared storage system 260.

As part of the successfully processing the set of one or more groups ofdata, the second indexer 206B can obtain or download the set of one ormore groups of data from the first location in shared storage system260. Further, as part of the successfully processing the set of one ormore groups of data, the second indexer 206B can determine informationabout the set of one or more groups of data (for example, host, source,sourcetype), extract or identify timestamps, associated metadata fieldswith the set of one or more groups of data, extract keywords, transformthe set of one or more groups of data, identify and organize the set ofone or more groups of data into events having raw machine dataassociated with a timestamp, etc. In certain cases, the second indexer206B organizes the events into buckets and stores the buckets. In someembodiments, the second indexer 206B uses one or more configurationfiles and/or extraction rules to extract information from the events orthe set of one or more groups of data. In some cases, as part ofsuccessfully processing the set of one or more groups of data, thesecond indexer 206B can generate one or more indexes associated with thebuckets, such as, but not limited to, one or more inverted indexes,TSIDXs, keyword indexes, etc.

In some cases, as part of the successfully processing the set of one ormore groups of data, the second indexer 206B can store the set of one ormore groups of data and the indexes in one or more files of the buckets.In addition, the second indexer 206B can generate additional files forthe buckets, such as, but not limited to, one or more filter files, abucket summary, or manifest, etc.

Fewer, more, or different blocks can be used as part of the routine2400. In some cases, one or more blocks can be omitted. In someembodiments, the blocks of routine 2400 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 24 and/or 26-29.

In certain embodiments, the cluster master 262 and/or the second indexer206B can delete the set of one or more groups of data (or the one ormore buckets that include the set of one or more groups of data) fromshared storage system 260. For example, once the second indexer 206Bsuccessfully processes the set of one or more groups of data, thecluster master 262 and/or the second indexer 206B can delete the set ofone or more groups of data (that was stored by the first indexer 206B)from shared storage system. In this way, the cluster master 262 and/orthe second indexer 206B can reduce the amount of data stored in sharedstorage system 260. In some cases, the cluster master 262 and/or thesecond indexer 206B delete the set of one or more groups of data basedon the location information received from the first indexer 206A atblock 2504. For example, the cluster master 262 and/or the secondindexer 206B can determine the location, in shared storage system 260,of the set of one or more groups of data based on the locationinformation.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 25 can be implemented in a variety oforders, or can be performed concurrently. For example, the clustermaster 262 can concurrently receive the data identifier and the locationinformation, etc.

4.2. Mapping Groups of Data and Indexers to a Bucket Map Identifier forSearching

As described herein, the data intake and query system 108 can index andsearch large amounts of data in a distributed fashion using one or moreindexers 206. In some cases, each indexer 206 can concurrently index,store, and search data. Due to a lag between the time at which data isreceived and the time at which the data is available for searching, thedata intake and query system 108 may receive a query indicating thatreceived (but unavailable for search) data is to be included as part ofthe query. For example, the received data may satisfy the filtercriteria of the query even though it was not in a state to be searched.In some cases, to provide the indexers 206 (also referred to herein assearch peers) additional time to index the data and make it availablefor search, a cluster master 262 can dynamically track what data isavailable for searching by different indexers and map the data to filtercriteria using a bucket map identifier. When a search head receives aquery, it can request a bucket map identifier from the cluster masterand send the bucket map identifier to the search peers that will beexecuting the query. The search peers can use the bucket map identifierto request the individual buckets that they are assigned to search. Bypassing a bucket map identifier between the cluster master (instead ofdata identifiers), search head, and search peers, the data intake andquery system 108 can provide the indexers 206 more time to make dataavailable for searching. In some such cases, if the data is madeavailable between the time that the search head 210 requests a bucketmap identifier and the time that individual search peers requestindividual buckets for searching (or any time before the cluster master262 tells the search peer what buckets it is to search), then the datacan be included in the search.

FIG. 26 is a data flow diagram illustrating an embodiment of data flowand communications between a variety of the components of a distributeddata processing system, such as the data intake and query system 108,for identifying one or more groups of data to be searched by a searchpeer. The data flow diagram of FIG. 26 illustrates an example of dataflow and communications between the cluster master 262, the search head210, and an indexer 206 (also referred to herein as search peer 206).However, it will be understood, that in some of embodiments, one or moreof the functions described herein with respect to FIG. 26 can beomitted, performed concurrently, or in a different order and/orperformed by a different component of the data intake and query system108. Accordingly, the illustrated embodiment and description should notbe construed as limiting.

At (1), the search head 210 receives a query, as described herein. Insome cases, the search head 210 can receive the query from a clientdevice 102. The query can be in a query language as described in greaterdetail herein.

At (2), the search head 210 identifies filter criteria associated withthe query. For example, the search head 210 can parse the query toidentify the filter criteria. As described herein, a query can identifya set of data and a manner of processing the set of data. The filtercriteria can be used to identify the set of data, whereas processingcriteria can be used to determine the manner of processing the set ofdata. Example filter criteria can include, but is not limited to,indexes (or partitions), hosts, sources, sourcetypes, time ranges, fieldidentifier, field-value pairs, and/or keywords, etc.

At (3), the search head 210 communicates an indication of at least aportion of the filter criteria of the query to the cluster master 262.In some cases, the search head 210 communicates a subset of the filtercriteria of the query to the cluster master 262. In certain embodiments,the search head 210 communicates all of the filter criteria to thecluster master 262. In certain cases, the search head 210 communicatesthe filter criteria that corresponds to the filter criteria used by thecluster master 262 to identify bucket map identifiers. For example, ifthe cluster master 262 uses an index identifier and a time range, thenthe search head 210 communicates the index identifier and the time rangeto the cluster master 262. If the cluster master 262 tracks additionalfilter criteria (e.g., host, source, sourcetype, etc.), the search head210 can communicate that filter criteria to the cluster master 262. Insome cases, the search head 210 communicates filter criteria to thecluster master 262 to request a bucket map identifier from the clustermaster 262.

At (4a), the cluster master 262 determines a bucket map identifier basedon the received filter criteria. In some cases, the cluster master 262determines a bucket map identifier by creating a bucket map identifierand/or by identifying a bucket map identifier that is associated withthe received filter criteria.

As described herein, as queries are received, the cluster master 262 canbe used to identify the groups of data that are to be searched as partof the query. This identification process can include comparing thefilter criteria of the search with information about the differentgroups of data stored in the data intake and query system 108. Forexample, if the filter criteria includes a time range of “last hour,”and index “main,” the cluster master 262 can review the cluster datastore 264 to identify all of the groups of data that are in the index“main,” and have a timestamp within the “last hour.” For each group ofdata identified, the cluster master 262 can identify a correspondingdata identifier that can be used to locate, retrieve, or otherwiseidentify the group of data for searching, and/or a search peer that isresponsible for searching the group of data.

To facilitate the identification of groups of data, the cluster master262 can associate filter criteria with data identifiers of the groups ofdata that satisfy the filter criteria and identifiers of the searchpeers assigned to search the groups of data. In some cases, the clustermaster 262 can identify these associations using a bucket mapidentifier. Thus, if the same filter criteria is used in a subsequentsearch, the cluster master 262 can efficiently identify the groups ofdata to be searched and the search peers that will be used for thesearch. If a search peer fails or becomes unavailable, or is no longerresponsible for a particular group of data, the cluster master 262 canupdate the information in the relevant bucket map identifiers.

Accordingly, as described herein, a particular bucket map identifier canbe associated with particular filter criteria, one or more particulargroups of data that satisfy the particular filter criteria, and/or oneor more particular search peer 206 associated with the particular groupsof data (e.g., the search peers assigned to search the particular groupsof data as part of a query).

Upon receipt of the filter criteria, the cluster master 262 can consultthe cluster data store 264 to determine whether the filter criteria ofthe query matches filter criteria from a previous search. If a match isfound, the cluster master 262 can use the cluster data store 264 toidentify a bucket map identifier that corresponds to the filtercriteria. For example, the cluster master 262 can compare the filtercriteria from the search with filter criteria stored in the cluster datastore 264. In some cases, if the cluster master 262 determines that thefilter criteria from the search matches filter criteria that is storedin the cluster data store 264, then the cluster master 262 identifiesthe bucket map identifier that is associated with the filter criteriastored in the cluster data store 264.

In some cases (for example, if the filter criteria cannot be found inthe cluster data store 264), the cluster master 262 can create a newmapping that maps, for example, the filter criteria from the search to abucket map identifier, one or more groups of data that satisfy thefilter criteria from the search, and/or one or more search peers 206responsible for searching the one or more groups of data. For example,using the filter criteria, the cluster master 262 can identify one ormore relevant groups of data, such as one or more slices of data (e.g.,in hot (“editable”) buckets) or other data (e.g., a warm (non-editable)bucket), to be searched and can associate a new bucket map identifierwith the identified one or more relevant groups of data. As an example,if the filter criteria includes a time range, the cluster master 262 canidentify one or more groups of data that include data that satisfy atleast a portion of the time range. For instance, if the time rangeincludes the last hour then the cluster master 262 can identify asrelevant all groups of data storing slices of data or events associatedwith timestamps within the last hour.

At (4b), the cluster master 262 identifies a set of search peeridentifiers. In some cases, search peer identifiers can correspond toindexer identifiers. In some cases, to identify the set of indexeridentifiers, the cluster master 262 determines which search peers areassigned to the groups of data that satisfy the filter criteria of thesearch. The indexer-group of data assignments can be stored in thecluster data store 264.

In some cases, the indexer-group of data assignments can be based onwhich indexers processed and/or stored the data. For example, if anindexer 206 processed a group of data then it can be assigned to searchthe group of data. In certain cases, a group of data processed by anindexer may no longer reside in a local or shared data store of theindexer. For example, after a predetermined amount (e.g., based on timeit was first stored or last time used, etc.) of time groups of data onan indexer can be deleted (but retained in the shared storage system260). In some such cases, the cluster master 262 can determine anindexer-group of data assignment for the group of data. In certaincases, the cluster master 262 can map the group of data to the sameindexer 206 that processed it. As another example, in some cases, agroup of data can be assigned to a different indexer 206 that did notindex, process, and/or locally store the group of data. In some cases,an indexer 206 can be assigned to a particular group of data based onthe availability of that indexer, or the unavailability of anotherindexer. For example, if the indexer that locally stores particulargroup of data becomes unavailable, another indexer can be at leasttemporarily assigned to that particular group of data.

At (5), the cluster master 262 communicates the bucket map identifierand/or the set of search peer identifiers to the search head 210 and, at(6), the search head 210 communicates the bucket map identifier to eachof the search peers 206 identified by the set of search peeridentifiers. In some cases, the search head 210 communicates the bucketmap identifier concurrently to each of the search peers 206 identifiedby the set of search peer identifiers. In some cases, the search head210 can also communicate a query, or a portion of a query, for thesearch peers 206 to execute. As described herein, in some cases, eachsearch peer 206 may only execute a portion of a received query. Forexample, a query can include a search across multiple search peers 206and the results obtained from each search peer can be further processedby the search head 210. Accordingly, a particular search peer may onlysearch a portion of the set of data of a search and may only execute aportion of the query.

At (7), the cluster master 262 updates the cluster data store 264 toassociate the bucket map identifier with an additional group of data. Asdescribed herein, a group of data can be a set of one or more slices ofdata (e.g., in hot (“editable”) buckets) or other data (e.g., a warm(non-editable) bucket).

In some cases (for example, similar to interactions (1), (2), and (3) ofFIG. 24), the data intake and query system 108 can receive one or morenew groups of data, such as a set of one or more slices of data, thathave not been indexed and/or stored in a warm bucket. In some cases, thenew group of data satisfies the filter criteria for which the clustermaster 262 has previously generated a bucket map identifier. In somesuch cases, the cluster master 262 can update the cluster data store 264to associate the new group of data with the bucket map identifier.

As another example (for example, similar to interactions (4), (5), and(6) of FIG. 24), an indexer 206 can process a set of one or more slicesof data and store the results locally and in shared storage system 260as one or more buckets. In some cases, the newly stored results maysatisfy at least a portion of filter criteria for which the clustermaster 262 has previously mapped a second bucket map identifier. In somesuch cases, the cluster master 262 updates the cluster data store 264 toassociate the newly stored results with the second bucket mapidentifier. In some cases, the cluster master 262 also updates thecluster data store 264 to disassociate the set of one or more slices ofdata from the second bucket map identifier. It will be understood thatthe cluster master 262 can update the associations (e.g., dataidentifiers, indexer identifiers, etc.) of a bucket map identifier atany time, and that the placement of (7) is for illustrative purposesonly. For example, the cluster master 262 can update the bucket mapidentifier associations whenever an indexer 206 fails or is added, newslices of data are received, hot buckets are converted to warm buckets,warm buckets are stored to shared storage system 260, warm bucket aredeleted from an indexer 206, and/or slices are deleted from the sharedstorage system 260, etc.

At (8), the search peer 206 communicates the bucket map identifier tothe cluster master 262. As described, the bucket map identifier can beassociated with one or more particular groups of data that satisfy theparticular filter criteria, and can be associated with the search peer206. The cluster master 262 consults the cluster data store 264 toidentify the particular groups of data with which the bucket mapidentifier and the search peer 206 are associated (and the dataidentifiers of the particular groups of data). In the illustratedembodiment of FIG. 26, the groups of data may correspond to buckets ofdata and/or slices of data.

As described herein, in some embodiments, a bucket map identifier maynot be associated with data that has not been indexed/processed (e.g.,slices of data or hot buckets). This may be due to the transient natureof the unprocessed/unindexed data (including partially indexed/processeddata). For example, the unprocessed/unindexed data remains so for arelatively short period of time, such as one second, etc. In some suchembodiments, the cluster master 262 can use the bucket map identifier toobtain a list of data identifiers corresponding to indexed/processedgroups of data (e.g., warm buckets) that are to be searched by thesearch peer 206, and use an indexer assignment listing to identify dataidentifiers corresponding to unprocessed/unindexed groups of data (e.g.,slices of data or hot buckets) associated with the search peer 206 thatare to be searched. In some cases, the cluster master 262 can identifyall of the unprocessed/unindexed groups of data associated with thesearch peer for searching. In certain cases, such as when the clustermaster 262 includes information about the unprocessed/unindexed data(e.g., time range, index, or other information that can compared withfilter criteria of a query), the cluster master 262 can identify asubset of the unprocessed/unindexed groups data associated with thesearch peer for searching (e.g., those portions that satisfy the filtercriteria of the query).

At (9), the cluster master 262 communicates a set of data identifiers tothe search peer 206 to execute at least a portion of the query. The setof data identifiers can include one or more data identifiers, and canidentify the particular groups of data with which the bucket mapidentifier and the search peer 206 are associated. For example, the dataidentifiers sent to a particular search peer 206 can identify one ormore buckets or slices of data that are to be searched by the particularsearch peer 206. After receiving the set of data identifiers, the searchpeer 206 can execute at least a portion of a query on the groups of datacorresponding to the set of data identifiers. In some cases, executingthe portion of the query on the groups of data can include applyingfilter criteria to one or more events of buckets or slices of data togenerate partial query results, and communicating the partial queryresults to the search head 210. As described herein, the search head 210can combine the partial query results from the different search peers206 to generate query results and return the query results to a user.

FIG. 27 is a flow diagram illustrative of an embodiment of a routine2700, implemented by a computing device of a distributed data processingsystem, for identifying groups of data for searching. Although describedas being implemented by the cluster master 262 of the data intake andquery system 108, it will be understood that the elements outlined forroutine 2700 can be implemented by one or more computingdevices/components that are associated with the data intake and querysystem 108, such as, but not limited to, the cluster data store 264, thesearch head 210, the shared storage system 260, the search peer 206,etc. Thus, the following illustrative embodiment should not be construedas limiting.

At block 2702, the cluster master 262 receives filter criteriaassociated with a query from a search head 210. As described herein, thesearch head 210 can receive a query and can parse the query to identifyfilter criteria. The filter criteria can correspond to at least aportion of the query. Example filter criteria can include, but is notlimited to, indexes (or partitions), hosts, sources, sourcetypes, timeranges, field identifier, field-value pairs, and/or user identifiers,keywords, etc.

At block 2704, the cluster master 262 identifies a bucket map identifierbased on the filter criteria. As described herein, a bucket mapidentifier can associate filter criteria with data identifierscorresponding to groups of data that satisfy the filter criteria.Accordingly, in certain embodiments, to identify the bucket mapidentifier, the cluster master 262 can compare the filter criteria ofthe search with the filter criteria of the bucket map identifiers thatit stores. If the filter criteria of the search matches the filtercriteria associated with a bucket map identifier, the cluster master 262can identify the bucket map identifier. As described herein, in certainembodiments, the bucket map identifier can identify the groups of datathat satisfy the filter criteria and the search peers assigned to searchthe groups of data that satisfy the filter criteria.

If the filter criteria of the search does not match the filter criteriaof any of the bucket map identifiers, the cluster master 262 can comparethe filter criteria with information about the groups of data stored inthe data intake and query system 108. As described herein, thisinformation can be stored in the cluster master data store 264 and caninclude data identifiers associated with information about groups ofdata associated with the data identifiers. If the information about aparticular group of data satisfies the filter criteria, its dataidentifier can be added to a set of data identifiers. The cluster master262 can generate a bucket map identifier and associate it with the setof data identifiers. The cluster master 262 can also track which searchpeers are responsible for searching which groups of data. Identifiersfor the search peers responsible for searching the groups of data thatcorrespond to the data identifiers of the set of data identifiers canalso be associated with the bucket map identifier.

At block 2706, the cluster master 262 communicates the bucket mapidentifier and a set of search peer identifiers to the search head 210.As described herein, the search head 210 can communicate the bucket mapidentifier and/or a portion of the query to one or more search peerscorresponding to the set of search peer identifiers.

At block 2708, the cluster master 262 receives the bucket map identifierfrom a first search peer 206 of the one or more search peers assigned toexecute the at least a portion of the query. As described herein, thecluster master 262 can, based on an identification of the first searchpeer, identify data identifiers corresponding to groups of data that areto be searched by the first search peer. For example, as describedherein, the bucket map identifier can be associated with groups of dataand search peers assigned to search the groups of data. Accordingly,based on the received bucket map identifier and the identification ofthe search peer, the cluster master can determine which groups of dataare part of the search and which groups of data from the search areassociated with the first search peer.

At block 2710, the cluster master 262 communicates a set of dataidentifiers to the first search peer. In some cases, each dataidentifier of the set of data identifiers is associated with the firstsearch peer, and the set of data identifiers identifies a group of datathat is to be searched by the first search peer. In some cases, thesearch peer can search the group of data locally using a local datastore. In certain embodiments, the search peer can download or obtain acopy of the group of data from the shared storage system 260 and thensearch the copy. In some embodiments, the first search peer can be thesame search peer that is processing the group of data (e.g., the indexer206 that is processing a slice of data as part of a hot bucket) or thesame search peer that processed and/or generated the group of data(e.g., the indexer 206 that generated the bucket), etc.

Fewer, more, or different blocks can be used as part of the routine2700. In some cases, one or more blocks can be omitted. In someembodiments, the blocks of routine 2700 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 24, 25, 28, and/or 29.

4.3. Search Recovery Using a Shared Storage System Following a FailedSearch Peer

As described herein, the data intake and query system 108 can index andsearch large amounts of data using one or more indexers 206 (or searchpeers 206). In some cases, each indexer 206 can store a copy of the datait is processing, the results of processing the data, or a copy of thedata that the indexer 206 is assigned to search, in the shared storagesystem 260. By storing the data in the shared storage system 260, theindexers 206 can improve data availability and resiliency. In the eventan indexer 206 fails or is otherwise unable to search data that it hasbeen assigned to search, a cluster master 262 can assign one or moresecond indexers 206 to search the data. In some such cases, the one ormore second indexers 206 can download the data from the shared storagesystem 260. In this way, the data intake and query system 108 candecrease the likelihood that data that is to be searched data will notbe searched due to a failed or unavailable indexer 206.

FIG. 28 is a data flow diagram illustrating an embodiment of data flowand communications between a variety of the components of a distributeddata processing system, such as the data intake and query system 108,for searching data following a failed search peer. The data flow diagramof FIG. 28 illustrates an example of data flow and communicationsbetween the cluster master 262, the search head 210, and two searchpeers 206A, 206B. However, it will be understood, that in some ofembodiments, one or more of the functions described herein with respectto FIG. 28 can be omitted, performed concurrently or in a differentorder and/or performed by a different component of the data intake andquery system 108. Accordingly, the illustrated embodiment anddescription should not be construed as limiting.

At (1), the first search peer 206A communicates a first bucket mapidentifier to the cluster master 262, and, at (2), the cluster master262 communicates a set of data identifiers that identifies one or moregroups of data that are assigned to the first search peer 206A. Theinteractions (1) and (2) are similar to interactions (8) and (9) of FIG.26, discussed above, and therefore will not be re-described.

At (3), the cluster master 262 determines that the first search peer206A is not available. As described herein, the cluster master 262monitors the search peers 206 (including the first search peer 206A) ofthe data intake and query system 108. Monitoring the search peers 206can include requesting and/or receiving status information from thesearch peers 206. In some embodiments, the cluster master 262 passivelyreceives status information from the search peers 206 without explicitlyrequesting the information. For example, the search peers 206 can beconfigured to periodically send status information updates to thecluster master 262. In certain embodiments, the cluster master 262receives status information updates in response to requests made by thecluster master 262.

In some cases, the cluster master 262 can determine that the firstsearch peer 206A is not available based on a determination that thefirst search peer 206A is busy or failing. For example, in some cases,the cluster master 262 can determine that the first search peer 206A isunavailable if one or more metrics associated with the first search peer206A satisfies a metrics threshold. For example, the cluster master 262can determine that the first search peer 206A is unavailable if autilization rate of the first search peer 206A satisfies a utilizationrate threshold and/or if an amount of available memory available to thefirst search peer 206A satisfies a memory threshold. As another example,the cluster master 262 can determine that the first search peer 206A isunavailable if an amount of available processing resources of the firstsearch peer 206A satisfies a processing resources threshold. As acorollary, in some cases, the cluster master 262 can determine that thefirst search peer 206A is available based on a determination that one ormore metrics associated with the first search peer 206A does not satisfya metrics threshold.

In the event an assigned search peer 206 (in this example, the firstsearch peer 206A) becomes unresponsive or unavailable (in some casesthis may happen after that search peer has been assigned to execute aquery on the group of data), the cluster master 262 can re-assign thegroups of data of the unavailable search peer 206 to one or moreavailable search peers 206, so that the one or more available searchpeers 206 can execute the query on the group of data. Accordingly, thedata intake and query system 108 can quickly recover from an unavailableor unresponsive component without data loss and while reducing orminimizing delay.

In some cases, the data assigned to the unavailable search peer 206A canbe re-assigned to a single search peer 206 (e.g., search peer 206B), andthat single search peer 206 can execute queries on the all of the datathat was previously assigned to the unavailable search peer 206A. Insome cases, the portion of the group of data assigned to the unavailablesearch peer 206A can be re-assigned to multiple search peers 206, suchthat multiple peers 206 are used to search the data that was previouslyassigned to the unavailable search peer 206A.

When updating the bucket map identifiers, any one of the other searchpeers 206 can be assigned. For example, a search peer 206 that wasalready going to be part of the query execution can be assigned, oranother search peer 206 that was not going to be part of the originalquery. In certain embodiments, the cluster master 262 assigns a newsearch peer irrespective of the search peers 206 used in the search. Insome cases, the cluster master 262 assigns the other search peer 206based on the status updates that the cluster master 262 receives. Insome cases, the cluster master 262 can prioritize search peers 206 basedon their utilization rate (assign search peers with a lower utilizationrate to the data identifiers of the unavailable search peer),involvement in the query (assign search peers that are already part ofthe query or search peers that are not part of the query), or whetherthe search peer 206 processes other data (e.g., assign a search peer 206that is set up to only execute queries), etc. Regardless, because thesearch peers 206 are able to download the relevant data from the sharedstorage system 260, the cluster master 262 can, in some embodiments,assign any one or any combination of available search peers 206 tosearch the groups of data that were previously assigned to thenow-unavailable search peer 206A.

Although not illustrated in FIG. 28, while the cluster master 262determines that the first search peer 206A is not available, the searchhead 210 can determine that the query has not been completed. Forexample, the search head 210 may have not received any search resultsfrom the first search peer 206A. In some cases, the search peers 206 canintermittently provide partial results for the data they are tasked withsearching. Along with the partial results, the search peers 206 canidentify which groups of data were searched or what portions of thequery have been completed. Accordingly, in the event the first searchpeer 206A stops sending partial results the search head 210 candetermine which portion of the query was not completed by the firstsearch peer 206A.

In certain embodiments, the first search peer 206A may have completedsearching at least a portion of the group of data. In such embodiments,the search head 210 can request the first search peer 206A to completethe rest of the search. In the event, the search head 210 determinesthat the search peer 206A is no longer available (e.g., by itself orafter consulting the cluster master 262), the search head 210 canconstruct a new query.

In certain embodiments, the new query can be a modified query. In thecase that the new query is a modified query, the search head 210 cangenerate the modified query based on the portion of the initial searchthat was completed. Thus, the modified query may include a subset ofgroups of data compared to the initial query and/or it may includealtered filter criteria. For example, If the initial search had a timerange of 0-10 and results from time 1-6 were received, the modifiedquery can include a time range of 7-10 (with other filter criteriaremaining the same). As another example, if the search head 210determines that ten groups of data were assigned to be searched by thefirst search peer 206A and the search peer 206A returned results forfour of the ten groups of data (in a time ordered or non-time orderedfashion), the modified query can indicate that the query is to be run onthe remaining six groups of data (with other filter criteria remainingthe same). By running a modified query, the data intake and query system108 can reduce time to obtain results. In embodiments where a modifiedquery is to be run, the search head 210 can combine the results of themodified query with the results of the initial query to provide finalresults to a user.

In some embodiments, the new query can be same as the initial query(e.g., the search head 210 re-runs the same query). For example, ratherthan attempting to identify what portions of the initial query werecompleted successfully, and re-running only those failed portions, thesystem can re-run the entire query. For example, once the cluster master262 has been updated to disassociate the unavailable search peer 206Awith the relevant groups of data, the search head 210 can re-submit thefilter criteria of the initial query to the cluster master 262 andrequest a bucket map identifier.

At (4), the cluster master 262 identifies a second bucket mapidentifier. Although not illustrated in FIG. 28, in some cases, thecluster master 262 can identify the second bucket identifier in responseto receiving filter criteria of the new query generated by the searchhead 210.

As described herein, when a search peer 206 becomes unavailable, thecluster master 262 can update bucket map identifiers associated with thenow-unavailable search peer 206. For example, if a bucket map identifierentry includes reference to the unavailable search peer 206A, thecluster master 262 can replace the reference to the unavailable searchpeer 206A with a reference to an available search peer 206B. Similarly,if the cluster master 262 indicates that the now-unavailable search peer206A is assigned to search one or more groups of data, the clustermaster can replace the reference to the unavailable search peer 206Awith references to one or more available search peers 206B. In some suchembodiments, where the cluster master 262 replaces reference to anunavailable search peer 206A with reference to an available search peer206B, the second bucket map identifier may be the same as the firstbucket map identifier albeit with different associations for the dataidentifiers, etc.

In certain cases, the cluster master 262 can discard any/all bucket mapidentifiers that include reference to the now-unavailable search peer206A. In some such embodiments, if filter criteria is then received thatmatches the filter criteria of the discarded bucket map identifier, thecluster master 262 may generate a new bucket map identifier byidentifying groups of data that satisfy the filter criteria as describedherein. In some embodiments, the cluster master 262 can automaticallygenerate new bucket map identifiers if one or more bucket mapidentifiers are discarded because they were associated with anunavailable search peer 206A.

In some cases where a modified query is generated by the search head 210and its filter criteria is received by the cluster master 262, thecluster master 262 can identify the groups of data as described hereinat least with reference to (4a) of FIG. 26. As the filter criteria ofthe modified query may be different this can result in theidentification or generation of a different bucket map identifier thanwas used in the initial query.

At (5), the cluster master 262 communicates the second bucket mapidentifier to the search head 210. As described herein, in some casesthe second bucket map identifier may be the same as the first bucket mapidentifier. For example, if the same search is to be re-run or the samefilter criteria used, the cluster master 262 can identify the samebucket map identifier. In some such embodiments, while the bucket mapidentifier may be the same, it may have different associations (e.g.,data identifiers associated with different search peers 206, etc.). Incertain embodiments, the second bucket map identifier may be different.As described herein, the second bucket map identifier may be differentbecause a modified query is being executed and/or the cluster master 262invalidated bucket map identifiers associated with the first search peer206A (and generated new ones), etc.

In addition, the cluster master 262 provides the search head 210 with aset of search peer identifiers corresponding to search peers that are tobe used to execute the query (or new query). As the search peer 206A isunavailable and no longer responsible for searching data associated withthe search, the set of search peer identifiers associated with thesecond bucket map identifier can be different from the set of searchpeer identifiers associated with the first bucket map identifier (evenif the first and second bucket map identifiers are the same).

In the illustrated embodiment of FIG. 28, the second search peer 206Bhas been assigned to search the data associated with the search peer206A. Accordingly, at (6), the search head 210 communicates the secondbucket map identifier to the second search peer 206B (along with anyother search peers associated with the query as the case may be). Asdescribed herein, in some cases, the new query can include searchingonly data that was associated with the first search peer 206A. In someembodiments, the new query can include searching data associated withmultiple search peers (e.g., based on re-running the initial query orbased on the modified query).

At (7), the second search peer 206B communicates the second bucket mapidentifier to the cluster master 262. At (8), the cluster master 262communicates a second set of data identifiers that identifies at least aportion of the one or more groups of data. The interactions, (7) and(8), are similar to interactions (8) and (9) of FIG. 26, discussedabove, and therefore will not be re-described.

As described herein, in some embodiments, in order for the second searchpeer 206B to search the relevant portion of the group of data, it mayhave to download the portion of the one or more groups of data from theshared storage system 260. For example, in cases where the second searchpeer 206B has not already searched the data, it may have to download itfrom the shared storage system 260. In some such embodiments, thecluster master 262 can provide the second search peer 206B with locationinformation of the data to be searched in the shared storage system 260.In embodiments where a modified query is executed, the search head 210can be used to combine the partial results corresponding to the initialquery with the results from the modified query.

In certain embodiments, if the unavailable search peer 206A becomesavailable again, the cluster master 262 can re-associate the groups ofdata that were previously associated with it. Accordingly, in someembodiments, the second search peer 206B can be temporarily assigned toone or more groups of data associated with the first search peer 206A.In some such embodiments, the cluster master 262 can use one or morebucket map identifiers for when the first search peer 206A is availableand a different set of bucket map identifiers for when the first searchpeer 206A is unavailable. In certain embodiments, the bucket mapidentifiers for when the first search peer 206A is unavailable can begenerated before or after determining that the first search peer 206A isunavailable.

FIG. 29 is a flow diagram illustrative of an embodiment of a routine2900 implemented by a computing device of a distributed data processingsystem. Although described as being implemented by the cluster master262 of the data intake and query system 108, it will be understood thatthe elements outlined for routine 2900 can be implemented by one or morecomputing devices/components that are associated with the data intakeand query system 108, such as, but not limited to, the cluster datastore 264, the search head 210, the shared storage system 260, thesearch peer 206, etc. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 2902, the cluster master 262 receives a bucket map identifierfrom a first search peer 206A. As described herein, in some embodiments,the cluster master 262 can receive the bucket map identifier in relationto a query received by the data intake and query system 108, which isprocessed by a search head 210. In turn the search head 210 can obtainthe bucket map identifier from the cluster master 262 and distribute itand a portion of the query to search peers (including the first searchpeer 206A) for execution.

As described herein, the bucket map identifier received from the firstsearch peer can be used to identify a set of data identifierscorresponding to one or more groups of data (e.g., one or more bucketsof data, slices of data or other types of data) that are to be searchedby the first search peer 206A. At block 2904, the cluster master 262communicates a set of data identifiers to the first search peer 206A.

At block 2906, the cluster master 262 determines that the first searchpeer 206A is not available. As described herein, the cluster master 262can determine that the first search is not available based on a missedstatus update from the search peer. Separately, the search head 210 candetermine that the first search peer did not execute the at least aportion of the query. For example, the search head 210 may not havereceived any results of the query from the search peer 206B and/or thesearch head 210 may have only received a portion of the results that itwas expecting from the search peer 206B. In some cases, as the searchpeer 206A searches the group of data assigned to it, it provides resultsto the search head 210 along with an identification of which portion ofthe group of data has been searched (e.g., an identification of thebucket that was searched to provide relevant results). Based on theresults received, the search head 210 can determine what portions of thegroup of data was searched by the search peer 206A. In certainembodiments, the cluster master 262 can perform the functions describedherein with respect to the search head 210.

At block 2908, the cluster master 262 assigns at least a portion of theone or more groups of data to a second search peer 206B. In some cases,the portion of the one or more groups of data can correspond to thegroups of data that were not searched. As described herein, when thecluster master 262 determines that the first search peer 206A is notavailable, it can assign a different search peer 206B to be responsiblefor searching the data that was previously assigned to thenow-unavailable first search peer 206A. In certain embodiments, thecluster master 262 can assign all groups of data associated with thefirst search peer 206A with the second search peer 206B or with multiplesearch peers. In making new assignments, the cluster master 262 mayretain the same bucket map identifier for a particular filter criteriaand/or it may generate a new bucket map identifier.

Concurrently, the search head 210 may determine that the search was notcompleted by the search peer 206A. In some cases, the search head 210may provide multiple requests to the search peer 206A for the missingsearch results. Based on the determination that the search was notcompleted, the search head 210 can run a new query. The new query can bethe same as the initial query or a modified version of the initial query(a modified query). In embodiments, where the search head 210 runs amodified query that corresponds to a portion of the initial query, thesearch head 210 can determine which portions to of the initial query torun based on the portions that were not completed. For some types ofsearches, the search head 210 may track specific time ranges thathave/have not been searched. For other types of searches, the searchhead 210 may track which results it has received for the buckets thatwere searched. In either case, the search head 210 can determine whatportions of the query are to be re-run and generate the modified queryto obtain results for the portions of the query that were not completed.

The search head 210 can send the filter criteria for the new query tothe cluster master 262 and the cluster master 262 can return a bucketmap identifier for the new query. In certain cases, if the new query isthe same as the original query, then the cluster master 262 may returnthe same bucket map identifier as it had returned for the initial query(albeit with different search peers assigned to search the data). If thenew query had different filter criteria (e.g., uses a different timerange or identifies different buckets, etc.) or if the bucket mapidentifier was canceled (e.g., because it was associated with anow-unavailable search peer), the cluster master 262 can return adifferent bucket map identifier.

As described herein, the cluster master 262 can also provide the searchhead 210 with a list of the search peers 206 that are to be used in thequery. Similar to the description of (6), (8), and (9), above withreference to FIG. 26, the search head 210 can distribute portions of thenew query to the identified search peers 206 along with the bucket mapidentifiers, the search peers 206 can communicate the bucket mapidentifier to the cluster master 262, and the cluster master 262 cancommunicate a set of data identifiers to each search peer 206. However,as described herein, the group of search peers 206 used to execute thenew query can exclude the now-unavailable first search peer 206A.

Fewer, more, or different blocks can be used as part of the routine2900. In some cases, one or more blocks can be omitted. In someembodiments, the blocks of routine 2900 can be combined with any one orany combination of blocks described herein with reference to at leastFIGS. 24-27.

5.0. TERMINOLOGY

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor, will cause a computingdevice to execute functions involving the disclosed techniques. In someembodiments, a carrier containing the aforementioned computer programproduct is provided. The carrier is one of an electronic signal, anoptical signal, a radio signal, or a non-transitory computer-readablestorage medium.

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, e.g., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. In certain embodiments, one or more of thecomponents of the data intake and query system 108 or 108 can beimplemented in a remote distributed computing system. In this context, aremote distributed computing system or cloud-based service can refer toa service hosted by one more computing resources that are accessible toend users over a network, for example, by using a web browser or otherapplication on a client device to interface with the remote computingresources. For example, a service provider may provide a data intake andquery system 108 or 108 by managing computing resources configured toimplement various aspects of the system (e.g., search head 210, indexers206, etc.) and by providing access to the system to end users via anetwork.

When implemented as a cloud-based service, various components of thesystem 108 can be implemented using containerization oroperating-system-level virtualization, or other virtualizationtechnique. For example, one or more components of the system 108 (e.g.,search head 210, indexers 206, etc.) can be implemented as separatesoftware containers or container instances. Each container instance canhave certain resources (e.g., memory, processor, etc.) of the underlyinghost computing system assigned to it, but may share the same operatingsystem and may use the operating system's system call interface. Eachcontainer may provide an isolated execution environment on the hostsystem, such as by providing a memory space of the host system that islogically isolated from memory space of other containers. Further, eachcontainer may run the same or different computer applicationsconcurrently or separately, and may interact with each other. Althoughreference is made herein to containerization and container instances, itwill be understood that other virtualization techniques can be used. Forexample, the components can be implemented using virtual machines usingfull virtualization or paravirtualization, etc. Thus, where reference ismade to “containerized” components, it should be understood that suchcomponents may additionally or alternatively be implemented in otherisolated execution environments, such as a virtual machine environment.

Likewise, the data repositories shown can represent physical and/orlogical data storage, including, e.g., storage area networks or otherdistributed storage systems. Moreover, in some embodiments theconnections between the components shown represent possible paths ofdata flow, rather than actual connections between hardware. While someexamples of possible connections are shown, any of the subset of thecomponents shown can communicate with any other subset of components invarious implementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Any claimsintended to be treated under 35 U.S.C. § 112(f) will begin with thewords “means for,” but use of the term “for” in any other context is notintended to invoke treatment under 35 U.S.C. § 112(f). Accordingly, theapplicant reserves the right to pursue additional claims after filingthis application, in either this application or in a continuingapplication.

What is claimed is:
 1. A method, comprising: receiving one or moremetrics of an indexing node of a distributed data intake and querysystem; based on the received one or more metrics, determining that theindexing node satisfies a node capacity threshold; based on thedetermining that the indexing node satisfies the node capacitythreshold, causing the indexing node to request a message payload from aremote message bus; receiving the message payload from the remotemessage bus; extracting a plurality of events from the message payload,wherein each of the plurality of events comprises machine data generatedby one or more computing devices in an information technologyenvironment; adding the plurality of events to a data buckets; andstoring at least one copy of the data bucket to a remote shared storagesystem.
 2. The method of claim 1, wherein the one or more metricscomprise at least one of CPU utilization and available memory.
 3. Themethod of claim 1, wherein the node capacity threshold corresponds to anability of the indexing node to process an additional message payload.4. The method of claim 1, wherein determining that the indexing nodesatisfies a node capacity threshold comprises determining that theindexing node has sufficient resources to process at least one moremessage payload.
 5. The method of claim 1, wherein the message payloadis a first message payload, the method further comprising: concurrentlyrequesting a second message payload with the first message payload basedon the determining that the indexing node satisfies the node capacitythreshold.
 6. The method of claim 1, wherein the plurality of events aregenerated by one or more ingestion nodes, the method further comprising,based on the received one or more metrics, instructing the indexing nodeto complete processing events that the indexing node is processing andshut down, wherein the indexing node shuts down independent of the oneor more of ingestion nodes.
 7. The method of claim 1, wherein theplurality of events are generated by one or more ingestion nodes, themethod further comprising: based on the received one or more metrics,instantiating an additional indexing node; and configuring theadditional indexing node to request a message payload from the remotemessage bus.
 8. The method of claim 1, wherein the plurality of eventsare generated by one or more ingestion nodes, and wherein the indexingnode is one indexing node of a plurality of indexing nodes of a dataintake and query system, the method further comprising, based on thereceived one or more metrics, increasing or decreasing a quantity of theplurality of indexing nodes, wherein the quantity of the plurality ofindexing nodes is increased or decreased independent of a quantity ofthe one or more of ingestion nodes.
 9. An indexing node of a data intakeand query system, the indexing node comprising: memory; and one or moreprocessing devices communicatively coupled to the memory and configuredto: receive one or more metrics of the indexing node; based on thereceived one or more metrics, determine that the indexing node satisfiesa node capacity threshold; based on a determination that the indexingnode satisfies the node capacity threshold, cause the indexing node torequest a message payload from a remote message bus; receive the messagepayload from the remote message bus; extract a plurality of events fromthe message payload, wherein each of the plurality of events comprisesmachine data generated by one or more computing devices in aninformation technology environment; add the plurality of events to adata bucket; and store at least one copy of the data bucket to a remoteshared storage system.
 10. The indexing node of claim 9, wherein the oneor more metrics comprise at least one of CPU utilization and availablememory.
 11. The indexing node of claim 9, wherein the node capacitythreshold corresponds to an ability of the indexing node to process anadditional message payload.
 12. The indexing node of claim 9, wherein todetermine that the indexing node satisfies a node capacity threshold,the one or more processing devices are configured to determine that theindexing node has sufficient resources to process at least one moremessage payload.
 13. The indexing node of claim 9, wherein the messagepayload is a first message payload, wherein the one or more processingdevices are further configured to concurrently request a second messagepayload with the first message payload based on a determination that theindexing node satisfies the node capacity threshold.
 14. The indexingnode of claim 9, wherein the plurality of events are generated by one ormore ingestion nodes, wherein the one or more processing devices arefurther configured to, based on the one or more metrics, cause theindexing node to complete processing events that the indexing node isprocessing and shut down, wherein the indexing node shuts downindependent of the one or more ingestion nodes.
 15. The indexing node ofclaim 9, wherein the plurality of events are generated by one or moreingestion nodes, wherein the one or more processing devices are furtherconfigured to: monitor, instantiating an additional indexing node; andconfigure the additional indexing node to request a message payload fromthe remote message bus.
 16. The indexing node of claim 9, wherein theplurality of events are generated by one or more ingestion nodes, andwherein the indexing node is one indexing node of a plurality ofindexing nodes of the data intake and query system, wherein the one ormore processing devices are further configured to, based on the one ormore metrics, increase or decrease a quantity of the plurality ofindexing nodes, wherein the quantity of the plurality of indexing nodesis increased or decreased independent of a quantity of the one or moreof ingestion nodes.
 17. Non-transitory computer-readable mediacomprising computer-executable instructions that when executed by one ormore processing devices of an indexing node, causes the one or moreprocessing devices to: receive one or more metrics of the indexing node;based on the received one or more metrics, determine that the indexingnode satisfies a node capacity threshold; based on a determination thatthe indexing node satisfies the node capacity threshold, request amessage payload from a remote message bus; receive the message payloadfrom the remote message bus; extract a plurality of events from themessage payload, wherein each of the plurality of events comprisesmachine data generated by one or more computing devices in aninformation technology environment; add the plurality of events to adata bucket; and store at least one copy of the data bucket to a remoteshared storage system.
 18. The non-transitory computer-readable media ofclaim 17, wherein the one or more metrics comprise at least one of CPUutilization and available memory.
 19. The non-transitorycomputer-readable media of claim 17, wherein the node capacity thresholdcorresponds to an ability of the indexing node to process an additionalmessage payload.
 20. The non-transitory computer-readable media of claim17, wherein to determine that the indexing node satisfies a nodecapacity threshold, the computer-executable instructions cause the oneor more processing devices to determine that the indexing node hassufficient resources to process at least one more message payload.